{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pima Indians Diabetes Data Set 分类器练习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import log_loss  \n",
    "\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据 & 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "# path to where the data lies\n",
    "dpath = './data/'\n",
    "data = pd.read_csv(dpath +\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = data['Outcome'].values\n",
    "x = data.drop('Outcome', axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = x.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据和测试数据分割(随机选择 20%的数据作为测试集);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=33, test_size=0.2)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.881107</td>\n",
       "      <td>120.747557</td>\n",
       "      <td>68.939739</td>\n",
       "      <td>20.731270</td>\n",
       "      <td>80.539088</td>\n",
       "      <td>31.927036</td>\n",
       "      <td>0.470352</td>\n",
       "      <td>33.333876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.346816</td>\n",
       "      <td>32.298361</td>\n",
       "      <td>20.115359</td>\n",
       "      <td>15.947951</td>\n",
       "      <td>117.867890</td>\n",
       "      <td>8.129285</td>\n",
       "      <td>0.330053</td>\n",
       "      <td>12.009142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>32.350000</td>\n",
       "      <td>0.365500</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>0.611500</td>\n",
       "      <td>40.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   614.000000  614.000000     614.000000     614.000000  614.000000   \n",
       "mean      3.881107  120.747557      68.939739      20.731270   80.539088   \n",
       "std       3.346816   32.298361      20.115359      15.947951  117.867890   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   34.000000   \n",
       "75%       6.000000  140.000000      80.000000      32.000000  125.000000   \n",
       "max      15.000000  198.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age  \n",
       "count  614.000000                614.000000  614.000000  \n",
       "mean    31.927036                  0.470352   33.333876  \n",
       "std      8.129285                  0.330053   12.009142  \n",
       "min      0.000000                  0.078000   21.000000  \n",
       "25%     27.100000                  0.245000   24.000000  \n",
       "50%     32.350000                  0.365500   29.000000  \n",
       "75%     36.800000                  0.611500   40.750000  \n",
       "max     67.100000                  2.420000   81.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JTdXsMNgPfBxYB5xF8eXfkZnD5fYhYMbxTtLTM43OzsmVFan21dvb3eoSpLqq/mw2Owy+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\nBpIkDANJEoaBJAnDQJKEYSBJAv4fREbGDDM8BFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d49910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outcome 分布，看看各类样本分布是否均衡\n",
    "sns.countplot(y_train);\n",
    "pyplot.xlabel('Outcome');\n",
    "pyplot.ylabel('Number');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输入属性的直方图／分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T/fIupWzJN1F3XURcRBk2f7VuvWqqpC8zr6lba5C1lIDZF3gH8NUGfkdPowT/IYNqjmnr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bab4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x_train.Pregnancies);\n",
    "plt.xlabel('Pregnancies');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MTyElycu/bz5p6xaZQUUzZ7AGeAVAVbeLyLKIsnlAlao2AojIZmAt8AvgOPAh4NmI5y8F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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bab4050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  血浆葡萄糖浓度直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.Glucose.values, bins=30, kde=True)\n",
    "plt.xlabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tyAWMmluMFXvuScqkUha8WIflYM/D76Otjswb4jvrpfoTleZdjKxr2wn4mj2x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\nulJKJYgGdaWUShAN6koplSD/D1ZcCPeB8TixAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116eb57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"Glucose\"].values,color='purple')\n",
    "plt.title(\"Glucose\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分居于中值附近，极少数存在0的情况，可能是数据缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SuIuIuJDCXUTEheI+t4wxJgf4Gc7Y+BTgPmvtztAom+/iTGvwe2vtv8WxzIjG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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e1ed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  三头肌皮肤褶层厚度直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.SkinThickness.values, bins=30, kde=True)\n",
    "plt.xlabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zc8Y6K1h61wr2Pv6WrZCITIi68mm30z69rpP0tPZQUVvhauZjF+eHVJJEZyJH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\nSqgrFApFGfH/A/aUe9YrmFnMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a08eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"SkinThickness\"].values,color='purple')\n",
    "plt.title(\"SkinThickness\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T2xvKdRhpSZzZMzEZp707xNrGAOHwRK7DmZXf5yU0Grloe7DSS89AmJNnhi60G+Tq914I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\nSp6iPM4tBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5d8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#舒张压\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.BloodPressure.values, bins=30, kde=True)\n",
    "plt.xlabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iwc5dUlWKk1vp5is1Jt4yWbVt0hVZqZSudJlttVsx56G5uOmFWzHl9lrdaWmZ\nApVIR58HhP4D0PW+J9w0CSOnjkoyuRipR6k5S641rmX+FO/p7gjqjnoWcZW7cfuWOzHnoblo2qzd\nt6IR5XEgH2OFJYXU/kbbdZWa3QBg8vLJ1HxkygSTU+YXoD/SrnlzI7qak09M18LX4sXGpS+i+7yy\n0qGvzYtwTxh2d0GSZ4v4bCWRIqVrAMHG7m/1xfUgjjx/CIefPRg3a6z85c3xZ6eiAa8W7CMuy8MU\n9zU9x5v523w49vujCSaVhKi6aBR8gJJ+m7BUXfDjRdjxLx9oehjFs221xAeg1W4F98phuCo8GDW9\nDL1dvdSozHREiortec3aedi1dhtVlE2eH7XIVpopUAt5dKlW9Kzis2P9x0hUq9F69Lf58PKCZ6la\n40rmT7m5z1lON4XRCJ4LIOQNIRQLZqNhsVsQDUfR161s3qNFdsvHut1pp7qCJpndFlZT85Mp8TJT\nLo2EEAeAZwBUA+gD8HUAYQAbIPyEHQLwDY7jVJ3aUoko/XjdDnzyq0/M3k7F4S5QXLrNvG92kjeD\nHrfAHQ9+kOQpI2XOt+fgioevi6enx1VLCb1eDUq4x+o73kwKWVOH+n+fjz3rdoB7mX7QgpRR08sS\nxLNSYfo9szDr765QdI9LNVJUZOZ9ggSAnnoV81NQUoBXl7yg69lKfUoN0SXQyDNE5P1Hb+RrOupR\niljmVPqrHLFsAKj5FSd0PenI6yPVvNLE6IxGhEtRc2k0a35ZDsDOcVw9gP8E8AiAnwBYy3HcPAjv\nhitNpq0JH+Rx/B31nW3zGzb6lmZAv+ucfNkqzefJ99U9F7g3uHi6ZjTgxeekYm6YcNMkVC8xZtsT\nxZ/a9ugXgaIJrJmheWsTdUJKl8lCj1lKnh8105sco4JsokugkWeIKImGKWnMy5+XjnqUoqU/bwY9\nomk0HSildKSkx5SnPP9m6vB4s+aXYwDshBArgBIAPIBrAGyPfb8JwBIAr6slMny4E3a78ei7zoZO\ndLWom14ifRHM+PIMnPzwJPxn/HCPccOn4u/sGedB1YIqHHrxkOL3vjYvbD4eZVUjwAd5+M744Cnv\nD0rpbOyEBRa4x7jR09UDT7kHPi+vuXztaulCURgYUSa4T6385c0oLi4A9waHrpYulFaWYtLySZjz\nT3NQGhPNkj5brA+j5gaLzYLSylKQlQRLHluCzoZOdf9/hfroOx009Fw9A6vAXYCQDhEkX5sXvQ1d\nKLtG2D9TMCkRAAAgAElEQVTwnfGhqLQoXvcrf3kzLL0RHHhGf5nk6IlbkF5bFAY8riLM++d6FNht\nOP72caGfUortP+2DzcfDFkZC3vUM9OU/XgpLKIKT206iq6ULBa4CWCwW9Pp74/8PBUIJbWy1W5P6\nrhz599L+eKn5kq6Qd6160ttvpG+4hSWFmPmVmbBYLTj21rH42JCWDVAeP2Nmj8HR149Sn+Me48a0\n1dMS0hHRGluecR74z/jhuswFP8VsxAdDmHX3LDR/2EzNdzoxO6n7IZhejgIYBeBmAPM5jhOb3Aeg\nVCuRixfNneDD24HS8aXoOkmf2B2uAjR9eBL+Nh+KR7tg9xRQBb6cY1yoXFiNpj+epEdBRoDnbvwd\niocXJ9hTi0oL0dXs7T/WKma/dlcKEYlaQTillaXosSMhcu6Kh6/DzPuvhr/Nh4NP7cPRt47hk//5\nBHZXso3ymrXzsOfRHbBYLNSNUDlyIaqOiwHwRVa4K/UHDFksFvz56b8IP2o6BaRo9W9z2rHi1dtR\n4C6A8zKnPtNCFHh20bOwu5Jt/OJ+xVX/NhcNHzQqm5X0aF5bdFwTw1XuxrZHduDk+02CO6dLUO8T\nnmWJB8VJsRc78NyNv0Ogza/7jNikg1fKPSCr6uKHkQfbA6iaPgbnz/sSzCvnz/tMn7E6+8FrEfD2\nILSJR1BFW14P7goPbBVOzXFhsVkw/Y7pmHTHNFht1gTz5qzvzEkoW8fFxDyJ40e8JuTrxdE/HFX+\nQbICt767Gp5xpUnpAMJco3Y04G3vrsFH63ehdecpalk8Y0tw9br5uBpQzbcRysroPvRmJ/X7AWzm\nOO5fCSGVAP4IoEDyvQfAJZNpa+JwOlC7shZ7f7aXeg3vD8Vt491nA6oHHThHOXH4ObrvuUigzY9A\nW/+vsfzQCwDxjiNGJI6aXqbq+05WEqoJ4fNnDiTY4+XnISodBKIH0dNB/jw9/tci0b4ojjyvvKqh\nUVhaiJ7OnqTP+4JhNLzBxe3LuvIRq2dpnSidszph+STltPRM1gZkDoqGFVHbCpR9K2kf1XtGbNLB\nK20+cC8dRmFJYTxSWTRFSDfgUjljFYDqvpARRA8yrTaO9kWx78l96LMk14OeyFTpNXsf3UVdYYyq\nK4NnHP39U21c1CybiH0//7PmnpLUzDIQJzqZffe/CEB8Te4E4ACwjxByfeyzZQB2ppY1dZY8tgR1\nX5mBorJiADFRHghvoQ63TjuVVVCK6+ky6PdugMD5ACb99RS4xgoRcKL/r6eyBDPvm40ljy1RvE+v\nLU/LTu1wF8DhdsBis8A91gOypg5XPVCveG39ugWYed9suMelduCw3eVA3d0z4aksgcVmgaeyBNPv\nmQVrEf0douHt43H7spgPT2WJ8LZs8nyMpk0NuOqB+v60rP39xCyusW6Mml4m1JFV0HqZfs+stPch\nJXu7WZdXrfvUbNxa+wquCjemrJqKurtnwlWR2MftTnu874n9XeodpqevmTmoRupGqVZ2q92KWzbe\nnnCPUhTwVQ/Ug6yug3ucJ6EsVz1QrzpG3eM8mPPtOUmeRZmOxDXr/eIG8DSAcghv6D8D8AmAJ2N/\nHwHwdY7jVCUPzXq/hHvCeHPFK2g/2C684ViBEVNGYslvbkI0Cry88Fndb1lFo5zouZDaQc56sNgt\nsBXYEO4OJ4h7jS4vVRQt0hIE0n4gsGKjIFIUCUew6+FtaNvdoksbXjxEwIwoF9AvBuUc7YovN4Pt\nATx/zdOqNlmyug4Lf7okQXTs9J5WvPPl11PKR2nNMPBBHmf2tuHt1a8ZT0jECtz69hp8/vR+tOw8\nhe5zQbgqPELk4SuHzbeVRt5F1PqE9Hq5EJbWfTe9cCu9XsTuQSmbc4zQtqK5KRzgYXfaBRfanjBc\n5UL9zF2/UFEAS09fk4u1yb12FMXKJOfb0tpGFOf7/JkDipHU8shZeVlUx6gVWL3tLtTOr4m3RTrP\nQk67oBfHcX4AqxS+yvyxJQBeW/5ioskhAnQe7cD7/7gJt769xpCYlNqETrMBmyEajiIcFvxjpeJe\nt/56heL1ztEuuCo8yeYdnXjG9YsU7Vq7LWGJqLXEdzgdGH1FuelzGkX9DekSOF4elc1q7uXDKCwV\nzAiRcAR7H90lBKKYnCylolQOpwMnN6fmxWAvduAPK15BNNyfIdH8YeQMVT0oCWqZOfNVz32Nb9M9\nydzl6u6uoo1dam6SykLIzUNy9PS1g0/tw9xHFmrKFdidBUkmSu6lw8KegUJgnL3YniTOp3aWq7ws\navXqGVsSP1RDJFNnIcvJqYhSQBBoopkcOo5cQLibT5sbllEVQ6PQltgdRy7A29ylKu2phTQS0syS\n3eF0mD6nUclVy+F0qEbiijS8dRzN7zdhx4Mf4LPf7NOl0KiWDwDxZbVRYSw54QCfMKFLoUUpmqVy\nYRWARFEuNZe9yoVVaP/0DLo7kl9S1O6rWlyDU9tOUvNRfs1YjF9YbSjvSmiJyZVfTY9Wbn6/Cbse\nFnzF/S1eINI/6R58an/8szDFaypCa7MoTEU3N74jnCeqVa96o3NTOQtZiZzSU4+EI9j89bfRpBJG\nfPMrt6F5S2OC1rm1yIpIj/7XPTGa8NrvzcdH63cmnVMoRg+6yt0oLC1E18kuhCnRlGpYbBZ8k/sm\n+koccREjab7NINd11zLjSJe2IqrRqTHsLuEIMu8pH1VTXk68jC98LpxeZBK72wELhIFiL3agryec\nsKKSiz/527yqol0ABDGrco+q2+tAI75hKgmvxaNLx7jQ1x1Gb1cvon1RWGwWjJ4xGiveXAW7ZA9D\nrPuEyEiXHe4KDy4dv6iREQCR/pWrq9xtOKIVFoCsqksQwEp401ZbEVoB12gTz9SRJyMeTlJEUyEg\ntIcYOS73Ylr5y5vRcTGg23Sml7zRU9+zbrvqhG6xWdD41vEkTxYjEzogaKNbrBbYi+yYu34h5jw0\nN8GOJ7fraUWN0nBXeOAp9+BSoAd71m1PWu6ZQcy7OKlq6TpLdchF9OhQhwNhdB7uUH22HKvdinmP\nfgFXfucavLzwOXPucVbgb965AyVVpQi2B3DgiU8Vte3Pfnw6wUSnNqGLLp7j60bjf/7q12mNoEwF\n8Q1TvkyX9slNX3sTnW39K9doXxRn95/Fa8tfxKo/fiX+udVuhcVqSYiWDgfC2hM6EJ+IxB9O4e2+\n2VA9WW3WJBOg3v7uGu1GoD3NEzqE8Wc0klpEaiqcu34hInwEh357IMmLqbi4AFc8fJ1p05kZcsb8\noscbZAQZiVMfNqfleTTxJemEDggmH7MCVaJ5oH3fWRz7A5dahiVIl3NGdZ1TjaDTo9lePNKJSSv0\nnYUqR7RVOpwOOEe7qBG7HYf1R6+KLp7OUcYjNQcSebsWlBTgIteheO2Fw+fha+2P40inyFnLtmbD\nprlIn/ld5KrFNVT99FSoXjLBcCS1FGlfp80B3BscujuCCLYHqHWW7sjSnHlT1xIXGjZlBBY/sRwv\nXf9sWp7nb/Ph5euf7Q/4CfDU/+uK+REXS1FhRTGidiT6QhH8ePSP07rBBiRqNUfCEdUITbmuc6pi\nWGqa7dI3eKmwlE/tqDEZ0gGgllc1G7er3I3guUCSmFUkHEE0EoXN6UBfmt3Nikc7EeUjSRryRpAL\nQHUcvkDfyI8AG5e+iMm3kn4t9db0rED8p32Yce9s8N08VW88CZNWXqvdiuOvHTU0Rhxuh+BlpiF8\nNuPe2YAFqpHU1csn4uS7yj+GvhYvvM1d2P8/n1Df9i+dvBQX+NIjRpcOcmZSV1u+uMrd+OKWLwGg\nR38ppjnGBavNAn8bpeGjsl19yv9puMrduHHDChx88i84trE/TDnaF0XH5xfQ8bm+t0mLzYIoAE+F\nB1WLa3BycwM9zzFEs8qeddtx7JUjuq4FtM01WjicBbo8bcRj4uY8NBedXAdeW/4ifYKyWeBRGABm\n8uqpLMHtW+5EyBtK0o7Z8t0taTGBKVE8vBidR5XfqvUiX6aPrBul6qHVfT4Yr/s5D82F3VWQ0n6N\nNB/usR7M/8ENaNvVost8YdaTLBKOIOI39is45fapuPwfrlQVPvNUlghCdgA1ktpTWYKF/7UYr352\njlrGrf/wLjo1VoXiD4sYrKgmRpcOcsb8orbTPPGWyZqCPkpMWjEFE26i6x2nysRbJmMEGYnTe0+n\nlM60r87El/Z8DWt2fhXzf3CDrjw3v9+kWzhJaoJJXcRJvyCa+LzRs8dQPY1G1o2Kl10MhZfea0bY\nqnikM0nMig/yQii5Aewu/QPy4vFOQ2krUbUo0aOieKRTl4dW06YGhLt5WFIVbomRIKC1XN85m5n2\nJJNy6oOTmuJqekTAxL6iZqKhmb/UUBOjSwc5dfKReOJM8FwQfCAEzzjhtJP6dQviUWxK15BVUzH6\nr8ag+3zyfZXXV6Onsxv+swHwgdTfYmBFPP2rHqjHuX1ncfB/95laflqLrKhaMgHX/ts8eMaVxE/f\nEcvoa/NRl6V8IITK66t1PTvk78XUNdNRNLwoIf3guSBC/l7YnQ4gqu22V7V0Ajq5DsXn8YEQatdM\ng63QBn+rD7ZCW8JpQmT1NDRvbUR3R3fcRDVqWhlWvvZF9PX0weEuSLheZPSVFbjUcBGX1CZNWZsE\nTvvjz+eDPPytPnR3dOPTxz821E6RcARVS2oQOONXPbEJQMpCWACw4L8WwzXaFc+zrdCGui/PQNPm\nBnSrxFuE/L0Yv7AaR144RM+HFXCNcaueGOQqd2PqndOSxpuvzYsOFakKsroOy55Zid5LPQi0B8B3\n83BXeDB+cQ1GTC1DyNeLkD+EwhFFVK1zOUWjiqnHHop9rWh4keacwQd5DJswHBG+D8HzQfDdPDzj\nSjDl9lpMv+dy2IvsGD55BA79L2UFZ6Jdpfkzi9rJRznl0ijCB3kUhYEeO1TlaGmRZ+Jn6XIjFBG9\nKNxjPf2Rba1e4bAHo0tPmwWW2ERqsVkwcuoo3PbuHXE3tUg4gh0PfoDDzx9SFItyuAuwZuddeGne\nM5r2SIvNgml3zcTcRxLfhOWbwp1cBzZ99Q2q14qrwo2Qt1fxee5xHlQvniAIXqnY2rs7gug4fAHD\np4zAvp//WZ8AVasXsCiLZim2idJhF+Ue8N5e5ePp9Ih/2QCbw4a+HuUg6nQEsrnGupME5eJl0BDH\nmnrndJz6UNljRawjNTE1V7kbq/74ZRSPdCZ9xwd5vDh3g6KJwj3Wg9Xb7+qv+xYvbMU2RPhov8+/\nFcLkqLN6HO4CFHgcCJxR7odKOuVKY1/pkI7iMS4UjyhGyBeK97vqxTU4ubXJkJnPOcaFkD+keDBN\nKjrqImoujTn1pi5ic9gwatww9PD0X3Wbw4ai4UUJb3fyz8SzECMhVTUD3dTeMQ21q6fho+/v7D/X\nETD3libt5FEgeC6I5q2NmPbVWQCEvB/67QFq2pFQH07v1mfvRBQ4t7896YxGaX3ZHDa4y93wt/mE\nM0sV4H0hxfMcAaBkfClObmnUPNfS4XSgpKoUH/9gt+pZrQlnZ4r1pYBim0QB3hsSViLi375QwpmY\nUkZNK0PwnIaURBSqhzDoSkMD3ifLs7QMGnk7/9k5lIwvUcyDWEcOpwO+li7F9p165zRMvFnZY8nm\nsMHX4lW8r/aOaTi9uyWhraLhaOIPsMHxEQn1qb6o1K6pQ83SRJOK0thXOpM37OfRfT6YeCbrvnZq\n3dEorR5GHXtK+TNK3pxRqpfujiBad55KiK6Tu9h1dwRx4s1j9ESsFsDSL0qU8H9rolCWVOCn48gF\n3QcrKGFz2qgCVh1HLsDX2qX7GUbc+oD+SDk14kJMY1WkP12OBPEjNcEr6ZmO4rNTEaCy2CzCxmrs\nudPunmXoUAaHuyBJuOm2d+9IEAUzMmrc4zyYed9s3LLxdpDVdXFht8Gg51IPpn9tVoLQmlRkCxDa\nd/rXZsFV7k6oR7JmWpKLr3RMSUXYjIhepROxruvXLYhHZtPcko3mSaw7tX4PCKvVybfXoudishop\nIIwNsnpaxsS8gBw1vwBIEi0CBAGh15a/iI4jF+LRdSNqR6J8zjic3NqYsGQNdnRrn4UYW3bbXQ4g\nGk0SKBL1qxOEhAy450mpXFSNa/5VCCh550762SLFZU7BfpqhZpuyaiq+8N9LNQWGOo5cUBVOm/w3\ntbjyO9fAXeERxLxUolpd5e7+Mx2XTcS0u2fhxbkbqNff/PJtePuO16hCSje/dBtObmqIm3o0o0kl\nWGwWrPrjV2AvsidtZsXFp27fqK/+rcAXt34Z3MufJ+ifj5tXibq7ZuL1m17KWDsqoSS0Ji2f3KRV\nPNqFouFF8LUkRg3Lo3WlprG+UF9C2ikL0+klJqA1fPKIJJOqYpS1hricHLHuwj1h1X4vCpyppm1F\nSmJeQB6aXwDA5SpEMJho/9y45HkhilBitug+H8S5fWeTlqxqG0JxYulE+IiwERZbpnccOo++njBq\nlk5E0fCiZHMLBYvNQm1sb+Ml2ItsmHwrwYEnPqVel0p4vZ58dHx+IckkooTD5cCxjUepZe48cgFW\nmwU1SyfCVmhTvZb3hxJMLIhGETzfTb3eXmxHd2eP4veecSVAJIpDvz3Q3+YG9ks840pwxf1z4LzM\nlbQxa3PYUFzmBPfqEfBKtneFtHh/CAf/d3+Cmafj0HnYC224eLyTaq7KBJ5xJZj9ravgcDqSTJMA\nkkxaYT+PngvdCebJSCiCc/vace4vZxVNY+KYENPWavt0lu2K++fgo+/vTDKpRkIRIb+xfm0rtOGz\np4yZXcW6KxxWpNGXdYxPFfOjXoaE+UVN6CsTiC563R1BNLylfl6qiJZbV9OmBtiLHRhORqYji1SG\nybRe5Jx48xja951VXLqK6HEnFOvIqOvhyc2NGH99FfX7lg/pEY1Vi2s0z4VVQyu6T68wmVZeGt45\njkiaRcC0yOQ5twD9HF+zwnBGqFk2EeFuXtWk2vhuv3nRqHunHhdIM6RbzAvIoeAjLVSj6zKAGDnZ\nsuOUtlDU2BLUxI6e2/7A+9QoPF+LF9sfeB89XT1JaaguXy3C8X2IHXWntdTtuaRs7xMJng3g90tf\nAKAu0lW/bgF6vb2q5RGjVaURpP7TPtVoP3+bD4HzdF0YX5sXIX8IdndB0hJ7+j2X49AGgzo8sTaq\nu20qZj94rebl9esWIBqJJohjWe1W2IoEvXwxUnDa3bOoeeluN75panfZUVo9DCFvKEFgrrujWzVy\n0lXuxsRbJqtGLqYaSQwkRyeL5pymWAi96GFidzkQ6e3rV0+MHQFpFrvLjrbdLTjx1nFVPSExIheA\n6kRqsVvim95iv5LvOwD9fbm4zKmuY6RSPnmUcDrIm0ldK7pOC1uxDX3d+pdj8shJJeRngQLQjMJT\niv6sXTMNrTtbqO5oS5++RbABX+ZE8FwQ73zpdVWvF7Wj/eTwfh4Hn9oPi9WSpPlstVs1yyNGq0oj\nSIPtAdVoPwBofq+RqlHucBbg2KuJ9SSKiQnRjvqjTKVtVFE1QvHAEjmiMNk1a+fB2yzoq4ja2XLh\nt1Sic+WEA2GMva4ySWCuuyMohKIrTOxqrohSUo0kFpFGJ8uF4cSxWbu6Dtd+b3687pyXOfHq4ucV\n+5Cr3I1ge0A1RiIcCOuKzpZG5KqVVerFpCRSJ+/L+37xZ+pxmKXjS7HoyZuw+W/fUnb5TLOYF5BH\n5he90XU0oryxH4NoVNsWWjS8CMMnj0hY8jqcDsMiQm07W6hL2HA3j01ffRMvL3wWry55AUeeP4jx\nGVjuNr57QvGoL63yiPfJsRerC40BQITyA007YLvhrePo6QxibP041XSljJ1XGf/RNXrMmMPpwMip\nozBy6qiEc0HF9k73Uh1A3OtJfA4f5BHyhlBzIz0qMuQNKXqA6NVqN4IYncwHeaqHVnNM116su+KR\nTmpk6rgF4zG8Nj3myAnLJ8XbyYhJiOYVJorKqYkITr55MkbPHkMtX7rFvIA8elMHgNvevYPq/dK8\ntTG+ZA0H+aQDkGlC+kDickxEeroLjQuHzmPPuu0Jb7haAltKiOYGi92adEiDtByizordJTSrnpWL\nrYgeMCPF3+qLCxPJd+5n3DubKooUbA/guSueQu2aaQkeE64KDxzF6t2vrzsMi12QQA53h+Eqd8NR\n7KCG3AfO+PHclU8DkSjsbkFwLdwdhr3YobhZarVbwb1yGK27WlA8rAi8L4Sulq6UPROkiEv1hreO\na+qBO8e4UOgpVJUUEJfrnsqShJOA5GJRomnm5NYmHNpwIF6ma9bOw0frdyYFdV2zdh6isXoTTVri\nea4JfUjDVOI/7YO/zYe//Pxj6upNyeSQIPDW5oXDWQCLBeBeOQxnuRuFwwvRe9H8ZqvD7UA0Iniw\nfbR+Z5JJSE0j3t/mw44HP0g4alFEy2w155/mIIpkk02mxLyAPHNpFBGjEkfWjYovO8WIMq1lvxy7\n004NR9aDPHps19ptmlrlchzuAkMeHEby9tdvfBG/X/6SKW3zmffNxtz1C9HdEcSG6b/O6J4GWVMH\nR7HDsG795NtrceajNlOa2WL50kF3R1BVQ945xoXV274Ce7EDL9T/FoHTyhOMe5wHd+y6G3sf3aXY\nj6bfMwsLH5qPbY/sUKyrUdPLEo+C1PjcKJ7KElQtqlFtJ7WISj7IY8eDH2iaNs1CK2fdXTM0NeKV\n+gMf5PHivGeogmDfOvpNXAr0JFyv5E5qFDWXxrwxv0gpHunEuHnjYS92xJeY4tI45A0Z2xCypnb6\nvK/Ni2B7IB4MYS4wKTOT5ZirK1A0wry2uag5HzwXzPgmdeuOU6Z060/vboX/tLnTjKSBUVLTkxEz\njdjuwXNBTFhG95qZtGIKikc6Be+am+mCbeIynuap0ry1CTaHDU3vKX+vdhRkOtDjfaRlcmjb05qW\nvChBC8jToxFP8+7REg2TXy8Xk0s3efmmrnZqd1+oj/rL6nAXoHBYIQJn/JpLfd1YgRG1owQtCROB\nSamuFLTyJh4rFo1EcfTlw8aP5bMCrnIPQl09adeFT8Ds0WMpHoXmHONC8Gyg33MjZtYRN0FpZhol\nXSHxCEDfKV985aXkXaR09Jz0Ol+LVz2Ya7QLgXYTp0qliMPtQM3ySTi28Qg1b+IxcDSzVjqClcyM\nGTHobMu9b1PHPO3YubiXj4JpZXR5qa7Nd6Oovann5aROM3GIyye178UdbaVj0vKZdC2/M4Wrwg2L\n1WLYjKLHHJAqSstyNTPb9HtmxTV8pJ5Rcvggn+BdIxWmo72YaEHbZ0mH4JgIzWvJPdaDO3bfrfqW\nmkrZpIiib3pJ1WwEKJtW1OapVBhS5hc9p3bTdCrq1y3QPCaNxshpozD9nlnxzaVcIx3Lb4e7AHZ3\nZpaVE2+erFu7W0rNsomY+8jChPY2ooOuB/mynA/yaFA5S/fklkaUVJXGvWZoyL1rpJ+b9VSh6tan\nVe9ceQxMuGmSptlB6/hFvRiZ0IH0mI0GwrSih5z1fhHtmvINB7XdaHHX3TnahRl/OxtX3D8HIW8I\nBSUFCHkFlT5Rz0Wv3d1itWBknSCLGzjjNxb4YgVKK0vh8BSgt6sXvjav6rKzuMyJ7vP0oJVJtxIU\nlBaieXMjAucC8FR4MP6GavBBHqf3xGzLlPTT8ZbGB0JY8uRNaHr3BJq2NMZlR6V6ISc3NyZ4Z0iP\n9qqOmYG4V44kmR2kXgLSZW61eCL9ew0Jp7l7KkviS2Cr3Yo5D83F1C/NACAM+Fdv+B21HEWjitFz\noVt3ueXeHMH2AAIqdnxawImRTTQjwVyA4O9dtbgG1cuE49laPmxOMBWIXjGi90kq5g8+EMKU22tx\nZu9pXZ4e8nKreVMBSDlYKY4kMFAtUAwQtIyk+U/XhmcmMG1+IYT8K4AVAAoA/ArAdgAbIFT3IQDf\n4DhOtWuYMb+I9qvmLY3oOpXsfqa2fJNress1tcW0rnqgHi8vfE57CWgRAoOu/6/Fms9OwgpMvrUW\nf/P0X8PbG0Kvtxc7HvwAJ/7AKU6w/Z4qLyN4ViHIpMKNmqUThbLFxJikutCucg8qrh2LM39qg78t\necLRu/zWPAIQwkQ85fapqLtrJqw2a5LpQB6go6R7r2R2EFHTyhd/oOW62dL9FTV9bFuxoJLZFzSg\nCyJblvNBHi9ctwEBhXoG+j1YxOvV9oC0XCr1eHUVj3aieHgxLh7v7Hf1JSOx+Inl8IxPrF+x7rUC\n2LRwj/OgeskEzLh3NtwVHsWJj1ZutfEnBtvRgnlE9PRn5xgXJiyfhLnrF6rutYnlmbB8EtUllNZW\ng2F+MSXoRQi5HsBqAIsBPA9gGYC7ATzCcdy//+IXv7gFgP1b3/qW6vlgZgS9RNGh3kvKOtuCtrOy\nJrRc01uuqS2m1dcTxvDJI6i64VIuHDqv69lJRAXRK76bR8X88fjo+ztx5Hf0k2lq19Rh8l/X4tir\nhxV1nW0OK07/qS1BjEmqC837Qug8fAElVaWK9+vV+y6tHobxN9SoljESiuD8/nbYC22oXT1NVdOe\npnvvLHPCWeZUPO1ITStfLlaVIFKlQx87Go4aDkST62PbHDb4W5X1xQGhH868d3b8b6U86hV7kpab\n1vfshTb4WnxJQndn/3w6IR9ies4yJ1UfXS8hbwjn9rXHRd2UoJVbbfzV3jENdV+aoZk/Pf2Z9/M4\nt689LkSmNnZD3hDaPz2L5q2NaNrUoLutlIQH00EmBL2WAjgI4HUAbwF4G8AVEN7WAWATgEUm06ai\nx14OQNFmXnfXDAR1LqmbNjXgqgfqEzS07S4HVedc6dlaussiR18/qq73bRWWflc9UC8IiFF0W3op\neuVyuju7MeX2WtOa4T2XeuJ1o1VGWhQqDaMRnXrSo9WrXFvcPdYjaOXrwYK41rhUj1ya/6seqI8H\ngSk9W492vB59eyli3xtWPSyh39P6RseRC4rRvgBw1QP1mHJ7LZxj3PH+L30TtTvtqLtrBmbce7lq\nP4wchv8AAB09SURBVKAJVmmN5aseqMecb89R1GfvarrU3wfHCc8W97JE/fcbfrVM0D+Pfa8208n3\n2tTKQ9t7UtpXSWdfNoJZm/ooAFUAbgZQA+BNAFaO48R3AR+AUq1Ehg93wm5PfhOj0dnQqWovLwoD\nI8qEBrn11yuEim3pwt7H9+LoG0cVzRa0tJywxtPwnfEh3B3G/8z6H8U3aaVnnzt0TrhewzbZdaoL\nf3r4Q7oNPwIcf/0ozn16BtULqqm2Wr028cBpP469dhQl40ow88szsezxZSgsKYznmw/yaP2oFc8u\nflbxfn+rUNZ4GWcq14l47cZFz8N3xofS8aWoXVmLJY8lu7NFwhFs+e4WHH3jKLpOdaleawS1/hI4\n48fCh+fD84ub4DvjQ8gXwq9n/1pXulf+w5W49p+vhafcEzfxyPNfvaAaYcp5m4Ez/nh/Ue3TbT58\nvG4HVjy5Qnc9SPusp9yD1o9acfhZZV2SaF8UfIsf42tHxz+LhCPY/M+bsX/D/vjRfg6XAzO/MhM3\nPHIDNn17E5r+2AT/WT/adpxC7cpafOntO/HrK36t2NflY0NEayw7YcWN/30jbnj0BvjO+OAqc2Hb\n97Zh46LfJfSRb37+DQTOB1BUWoRgRxB7H9+L4+8ex6ENB4RrVhDM+ac56PX24qlrnlLPY1UpVv7y\nZrweeB2Hnj9ErTNanovCwLDhroz0ZSOYndQ7ABzlOC4EgCOE9AColHzvAXBJK5GLF40p1fF2uhCP\nu8KDHjuS7Fe7frLHcARnUlolDoQNPjvssesWSDr62lGqGxgAIAJ0nezCgZMHqNcZckmLAN5TXhx4\n5gCihdYkd7zCiaWqed/+w12Y/8MbEPbY4arwUG3HAOCLfdd1sgt7f7YX3d0hTfc/tWuNoKe/9AV6\ngBIHdj+6QzM90a565dq56LNbhUjBQI9i/tXaStpf1PIIAAc2HEC0ILmN1Cgr86CvxIFLgR7YKpyq\nfeMvzx1AyeWXxf9WcsXkAzw+feJTNO86leD2KrZTwNdjeFzqaRsAQh2XOPDu/92s2kcC0b6ksd51\nsguf/OoThMJ9mPPQXF153LV2G3VCB+jjTEzjjW+8nZG+LKesjL6aMPvTsQvAjYQQCyGkAoALwAcx\nWzsg2Nh3mkybitHoLbMa0bRIMLVnA0hRIEmvK6TydWZd0qga2CpuZSe3NKJ931l4m7tQY1CcTGmZ\nqsekZga9/YUP8ji5Vd2djaypwx277o6feCWi3seU20r6bCO69GYoHunECBV9/pYPmxNMQWqumDTT\nQ/PWJmo0Js0N0MhY1tNHtMxYADSfp2e+oI0zcQ7IVF82gqk3dY7j3iaEzAfwMYQfhm8AaALwJCGk\nAMARABvTlksJov3y1JYmQXxJxV1KyzXROcYF5yhngludmuuVkiiP6Ib34rxnknbD5SJF9iIH9eQi\nPhgCWV2H03taVV3KpNfJ83DpZFc8elEvNPc6Nbcyf5svrrdud9kxom4k/C2++HJdLaJPyf1PywU1\nFa1pPUJKWv1kyqqpWPgT5eWz2r1KbaXUv7R06VOth8VPLMdL8ynmNEnaWq6YtLd9X5sXM+6dDYvN\nkhQFG41EEQlHFOtOr8iVnj4S6YtQVzuiINeCHy9SfZ5mP/jiVBR4ChLGmdTlVtCTz1xf1otpP3WO\n4x5Q+Dj9kmMyRC3jYT9dhuZDZ1X9RNU0oqU603p9TuU6ys7RriRhJVEpEQDmrl+YrCFO0Y32jC2J\n61CruZRJr5Pm4eBT+1VqjQ5Nz9ld4YG7Utt8FA6E0Xm4A1f+45WYuLoOQEwfm+JeJ3+eWhulQ2ta\nqc0A4UAHsb1V8zDWgwU/WkS1h6rdq9RWSv1LS5eeVg96+61nfCm1LaVpO0e7NM1pSjicBXCP9cBi\ntSQIz6lp8QPKbaNUDj195E//qW4+414+jMLSQtXnafWDAndB0ouOVG89031ZLzkbUaonekttiTfx\nlslxBUejkWDi9YC+5ZZ4vZputLh8C7YHUFJVqqm/rCcPelAyHYl5NmI+Ov7O8XiUZPFIp66lbleT\nsO1ixKRmBPEZ3R3B+AHhex/dhRfnPYPnr30aL857BrvWboOtwEbNg1YUpB4zgu6+qtE3xDaKhCPY\ntXZbUjloUZR6TYcAVI/qo2/0RRHuNm9G06ofPfnXMp9J80F7ntpzqpdOoEacil5KRs3DmSJnI0r1\nkkkdYzOmA3l+SitLUbmoOsmEU7N0Ambce3k8AtPM0pRKLJJOzXRktVuTzEdqnjzeVm9CeWn1fs3a\nedi1dltC8IbesupFDGppePcEApIoU6lWOJC4qqpftwDFxQU4/NoRw3lIVx/Ta94rKi1M2LCUrw5T\nSbtm6QRMv2dWUlRvXKhLgXB3GB2HLwyaGU3N7GE0H7TnqEWcSvXWlcb3+CU1GdFNp5GXgl5KZCKs\nV0tLWY/4T9X0MXj7/k2aAmO0fBsVQJIe30bT5JYLVIlRhm/f8RpV57u0qhSrtt+lGf2pR0wt1TYy\nqlkvtlVF1Qicbu40nYd09TFpOrQ2UkJaDtrY0JO22Bbyo/rU+vrtW+6kmty0xgINpTFOiybWMwaM\n5EMp8lnrGdJxIx3fUj31dDGkBL1oZEJsJ5Xlll4TDgDTS1MlJtw0Kb6Dr3e5LApLqel81/51LdXL\nQXr0Wipl1YMZjyfpgcSp9JN09TGzpjVpOVJJW2wL+VF9an1dj8ktHSjVsd4xYCQf8ucY9VIaTHGv\nvDK/SH9dAShqgaQbpeVa1eIaTLt7VtzOpobvjC/lZauW9wSQfKK8WdNRNBJV1Ple8tgSdFzsP6ld\nSd8l3BPW9Uyzb7x8kEf7p2cMm6MGchPLCEZNa0bKkQ7TodzMpNcMpaXtYwal4/AQO84wXSbXTHsp\npYu8mNQThIFavbC7CoBoBOFAOG5PdVem79xJKdIdfH+bDwef2pd0LiTtmZFwBB//5E+wWCyKhynr\nHaRa3hNKJ8qb2am32q2Y9+gXcM3aeUmDUtSvlp+bKRVMc8UiMGkBOUUji5Ps7XraTN7+FqtyfdIY\nyE0sI6i1kRJGymG2/dW8R7S+Vzo4ROmQEDPQvJzSaXI166U00OSF+WXPuu347Df7hA4aBcL+UPxg\naNG3VtxM2rNuu1pSpnE4Hfj8mQM49NsDQj4i2s/cs247PvnVJ1T/X6PLRZr3hNTTJ+H6FExHSjrf\nCe0QAQKtPlw4dL7/7zYfNWq2ZtlE/PlHexLu19tm8vbXiqx1uB1JOvrZiFobjZpepngeQDrSTlU3\nnPb9nnXbcfCp/Qmb1aLbY7rGpfTZGTO5animDTY5/6bOB3lD5342bWrAnIfmpr3ytezF8meqXW+x\nWTDtrplp8Z7QG0zla/PCNdqtuUylmUaM2LKlxwaKeRTlVpVQazPV58Z0t+Ua61c9UI+eju60vMGl\ncwNeKS21Nu0L9SWZuXwtXgxzFel6XqZPuJebQ9WiVRvfPZHSuExHO+iRhQYyX2+pktOTeiQcwY5/\n+cCQ7nOm7F5GbZRq10ejUcz6+ysML0f1BnNIr69ftwARPoKm9xoQaPfj5Nam+OfS52tpfqvtDcgJ\nd/O47Z01sBfZ43nsarpkam9B1e4cFQ4WqVk2EbP+/ooEXW9RxMwsqWigG02L1qZWuxWlNcPifuvi\n/aXjS1G1ZIJmXoz2l1TKM/a6caYODjHzLKPtoGU2lKeZqXpLFzk9qe9Zt111c1CJTNm9jNootSIR\nU8mj1MNBiz3rtiecy0jzeRZNHLTrPOUe3fZfe7E9aYPMbDSelt25+3wQh589CHuRPa2CSlr1ke60\n1NpUfn/XyS5DeTHSX/SgVB7upcOwu+xxs6gcs+MyHe0gTyPQ6kNA8qJISzPd9ZYuctamnk6xrnRg\n1EaZDdFnesW09FxnzLXSgnA3r1sArWpxDYLtAcWoRL3PTafedTpFyMykJc17JgXRzKBqVrTQp5sJ\ny7XPLzXyLL1lNzKPDEZ9miFn39S1lvu2Yhv6uvsUz6zMFGZs2majGNOBXpORnutQNUJ3BCrvD+GV\nL/wOgXY/VQBNeo6pljeReF/DW8ep53SK+fRUllCX63pJpwiZkbRoZo1sEJES0RI4m/LFqcLkqHIG\nbTqepbfsRtxGs8VlUYucndTVlvvusR588f0vJRwqPRB2LzM27Rv/+0bMvP/qQbHN6TV56L1OWn6t\ncy7FyVdNAO3AE5/qMg2Jz73i/jnCj4XCxC7mU225fuuvV6jUVj/pFG4ykhbNrKGm2z7QLnZaZsUF\nP16EBT9elBY/9XS0gxG30WxxWdQiZ80vasvuCTdNQvFIZ1xEa6Aju4y6Ug1W9JleE5AZ09LIqaOo\nrl9KyKPxnKNdVAEl2jK4eKQTE29RjnpNp951Ok1netNKVbd9oNAyo4muhkousel8lt6yO5zqZweY\nSXOwMXXwdLowc/C0iMtViBFXjkHI14vguSD4QAiecSWoXVOH+nULYLHqPXRicMnUwbR6GTe/Slcd\nal2nVA75Pa4x7gRpVil8IITaNdNQNFxwx/O3+vDpTz9SPCpPfq3e8gRO+1XTvPzuyxEt1He8ot56\nS1daavURCfeBrKpDyNsLPhDCsKphmLJq6qCNg3Hzq9Db1YNOrgMRvt8Gd/G4oKtTuaBKV770jI10\ntENJ9TAc+l+6dLWr3I2pd04zVZ+DcfB0Xgh6ZUKsa6AwKkyWKfTWIe06tXKI9xSUFOgWfUpFLI2W\nT600v3X0m4bFlzLtpy79Tqs+AGRURMoIaqJqcsE4GkbGRirtoFa3StHYRsjU+M57Qa/BFM/JF/TW\noZm6Fu8xIvqU6tLaqPCT2aV1OvueWlp68p4t40DrWLzGd0+k3YsklbKr1a1SNHa2k7MbpYzcxIiH\nkNa1Zt7Osj0aUI1U8j6Qq1mtY/H0eJHwQR6dDZ3g7RiQH6lc7hdy8sL8kssM1XIYmWTk16YjilDp\n+bnSFlp1Jy1HOiNfjeTvhes2UI/Fs7sd+Or++xSjegcjv1LS/eM3GOaXnN4oHcwNxnQxVMthc9hQ\nNLwINof2xqT82t3f+xCf/WYfQt5eIAqEvL1o//QsQr5ejP+CPk8GpefnSlto1Z20HOmoKzP587d6\n0f7pWcXvI6EI+nrCis8fjPxKMdIv9TAYG6V5YVNnDB2yLYIymxnMuqpftwDT7p5J87ZUfD5r2/TA\nJnVGTqErupUBYHDrymq34vJ/uJI6qSs9n7VtemCTOiOnECMAlciViL+BYrDryujzBzu/+QKb1Bk5\nRTYIoeUKg11XuShylw+k5NJICLkMwKcAFgMIA9gAIebtEIBvcBxHkXRiMMyTT+5nmWaw6yqVg1tY\n25rDtEsjIcQB4BUA0wCsAPAjAD/hOO5DQsgTADZzHPe6WhrMpZGVIxVyxf1soFEqx2BHXRt9Ph/k\nURQGegbITz1T5FpE6WMAngBwOvb3FQDEgwY3AViUQtoMhibZEkGZCyjVVSqa8ul4vtb1IyaOYG1r\nAlPmF0LI3QDOcxy3mRDyr7GPLRzHiW/ePgClWukMH+6E3W7eH7SszGP63myClSN7yIcyAOrliIQj\n2PLdLTj6xlF0nepC6fhS1K6sxZLHlgxIgI8R8qE9BroMZm3q9wCIEkIWAbgcwLMALpN87wFwSSuR\nixeDJh+f30vlXCQfypEPZQC0yyEX2+o62YW9P9uL7u5QWo/8S5V8aI8Mml+o35n6WeY4bj7HcQs4\njrsewH4AdwHYRAi5PnbJMgA7zaTNYDAyBwvwyX/Sudb6DoD/IIT8CUABgI1pTJvBYKQBFuCT/6Ss\n0hh7WxdhfkcMRhaTzqP4GNlJdu2KMBiMjMICfPIfpqfOYAwxWIBPfsMmdQZjiGG1WzF3/ULMeWhu\nzh4DyaDDJnUGY4giBgQx8gtmU2cwGIw8gk3qDAaDkUewSZ3BYDDyiCE3qQ+kiBGDwUiEjb/MM2Q2\nSgf7lHIGYyjDxt/AMWQm9T3rtieIGPlbvPG/s0nEiMHIR9j4GziGxE8kEzFiMAYPNv4GliExqTMR\nIwZj8GDjb2AZEpM6O6WcwRg82PgbWIbEpM5EjBiMwYONv4FlyGyUMhEjBmPwYONv4LBEo1HtqzLE\n+fM+0w83e0zUYJ+qLicfjuwC8qMc+VAGILvLYWT8ZXM59JLB4+wstO+GzJu6CBMxYjAGDzb+Ms+Q\nsKkzch8Wichg6GPIvakzcgsWichgGINN6oyshkUiMhjGYK86jKyFRSIyGMZhkzoja2GRiAyGcdik\nzshaWCQig2EcNqkzshYWichgGIdtlDKyGhaJyGAYw9SkTghxAHgaQDWAQgDrARwGsAFAFMAhAN/g\nOC6SllwyhixWuxVz1y/EnIfmZlUkMIORrZg1v3wZQAfHcfMA3AjgFwB+AmBt7DMLgJXpySKD0R+J\nyCZ0BkMds+aXVwFsjP3fAiAM4AoA22OfbQKwBMDraokMH+6E3W4zmQVBVyEfYOXIHvKhDAArRzYx\n0GUwNalzHOcHAEKIB8LkvhbAYxzHiQJdPgClWulcvBg083gA+SH2A7ByZBP5UAaAlSObyKCgF/U7\n094vhJBKANsAPMdx3AsApPZzD4BLZtNmMBgMhjlMTeqEkNEAtgB4kOO4p2Mf7yOEXB/7/zIAO1PP\nHoPBYDCMYNam/hCA4QD+jRDyb7HPvg3gcUJIAYAj6Le5MxgMBmOAMGtT/zaESVwOcx5mMBiMQYRF\nlDIYDEYewSZ1BoPByCPYpM5gMBh5BJvUGQwGI49gkzqDwWDkEWxSZzAYjDyCTeoMBoORR7BJncFg\nMPIINqkzGAxGHsEmdQaDwcgj2KTOYDAYeQSb1BkMBiOPYJM6g8Fg5BFsUmcwGIw8gk3qDAaDkUew\nSZ3BYDDyCDapMxgMRh7BJnUGg8HII9ikzmAwGHkEm9QZDAYjj2CTOoPBYOQRbFJnMBiMPIJN6gwG\ng5FH5MWkzgd5dDVdAh/kE/5v5D61zwYKtWcPRL4y/YxMpS9Pd6DbUOn5HUcuoOPIBc08pNpfUyVd\nYyCd5dDKE+1+Wr2beZ7e/Ku1NR/k0dnQOeBziT2diRFCrAB+BWAWgF4A93IcdyKdz5ASCUewZ912\nNG5qgL/VC7urABZEwQd5uMeWYMKyiahftwBWu5V+X5sX7rElqFk6AQDQtLkx/hnt/oyWQ/ZsANTv\n0pUvteen4xmZSl+erqvCg+JhRejp6h2QNlR6flFpIbqavQj7Q/+/vfOPkauq4vhndne6y87Obgtd\nti1tF0r1tAWrCFGgtF0Tan+gYIgS/kAEEouGVE1MVLCYQmqMifgH7h8CilXQmIjWaKXSRFEoSoyK\nUdLmNG2JUUt0uy10d6e7sz/GP97MdmY7b2bu7J2dvsn5JJvsvPvjnO95c8+7776XuQDEO+KsuvMq\n1j3aV+CDS0xqET9fY8CnjpnlXcu7WH7zFed8Chnj1+9czx8ffYnDPz5UEHe5Yw2xplioHte4zsw3\nze1xMmOTTE1MFZzrG76ygVd3v1zTMVuKWCaT8daZiNwO3Kqq94jI9cCDqnpbWP2BgaGqjXd3J9l7\n/y/4+5Ovlay3dvs13LT7AwXHDu58sWy7Uu19UkrH2u3XAISW+fIrLB4uNrq7kwwMDNWsf5d+q7VT\nSsNs7BfzwSUmrvGrRIevMeBTh4tP+Sy8upuTrw9UXL+cvTC9lfoX5o/PMdvdnYyFlfm+bNwE/BpA\nVV8FrvPc/zTjqXGO7z9Wtt4b+4+ddytWSbuw9r4p5c/xXx3l+PPFb3R8+VXKvg8bterf5TzW4hy6\nfo+OP3+0YHmm0pjUIn6+xoBPHWcHU04+5XPyUOUJvRJ7xfS6xGzw8MmK+60FXpdfgE7g7bzPkyLS\noqoTxSovWNBOS0tzVYZOHTvF8H/OlK03fGKItgm4uDvp1C6svW9K+TP8Zvhsy5dfJe072uguUs9n\n/5X2Oxs7xTTM1j7AyJvD0z64xKTa+JXS4WsM+NQxeSLl5FMBU27Vy9krptclZpnJ4gsQtc4lOXwn\n9TNAvsdNYQkd4PTpVNWG5i9O0nFZJ8P/Kh3ojiVJRluYvh0db6GidmHtfVNKR8fiJMRg+N/n2/bl\nV6l4uNgIu+X31b9Lv9XacVl+cf0eJRZ3TPvgEpNq4ldOh68x4FNH85J2J58KaMIpsZezV0yvS8xi\nzbGiid1nLil10fa9/PIKsA0gu6b+D8/9TxNvj7Ni65Vl612x9Uri7XHndmHtfVPKnxW3rGTFtpU1\n9auUfR82atW/y3msxTl0/R6t2LZy2geXmNQifr7GgE8dF13S7uRTPgvXdDvVL2evmF6XmF2yemHF\n/daC5l27dnnrrL+/X4HN/f39DwFbgE/v2LGj+AITkEqlqzaeSLRy8XWLSA+NkfpfivTwGPHEPJrn\nNZGZzJBc2smqO9dw466NxJoKnyks3dA73W58JE1yaSdyx2p63ruIswPnjoW198lMHTNtL+u7PLTM\nl1/F4uFqI5FoJZVK16z/SvrtuCxJ5/IumltbqrJTSkOl9pPLOpk4O8FUehII3oi46u61rHukr8AH\nl5i4xq8SHb7GgE8dM8vn987nHR9dNe1T2Bjf9MQtpIfGOH3kVEHc19z1LnquXRyqxzWu+fXTw2O0\ntMchA5mpTMG5/uCTH2J8JF3TMZtItD4SVub17RdXZvv2y/StXWqc1H9HaO9JAEz/X+6qmN8uV7fY\nsVoSpmOm7bnwazY2Klm6qJWGmf1Wa8f17ZdS9s/8M3i01NnbVdIHF18rreu0jORpDPjUkSvvvXoR\nb42MntcGio/xsLhXaq9SvTN9CTvX46lx2iZgtAXvY7bU2y8NkdSjjOm4cGgEDWA6LiRqpWEuX2k0\nDMMw6ogldcMwjAbCkrphGEYDYUndMAyjgajrg1LDMAzDLzZTNwzDaCAsqRuGYTQQltQNwzAaCEvq\nhmEYDYQldcMwjAbCkrphGEYDYUndMAyjgfC9SUbNmevNrX0hIu8Hvq6qfSKyEtgDZIDXgQdUdUpE\nPgncD0wAu1V1X90czkNE4sDTwOVAK7AbOESENACISDPwFCAEfn8KGCViOnKIyKXAX4BNBH7uIWI6\nROSvBJvrALwBfJWI6RCRB4FbgXkEuen31FFDFGfqHwHaVPUG4EvAY3X2pywi8gXgO0Bb9tA3gZ2q\nuh6IAbeJyCLgM8A6YDPwNRFprYe/RbgLGMz6uwXoJ3oaAD4MoKrrgJ0ECSSKOnIX2ieAs9lDkdMh\nIm1ATFX7sn/3EjEdItIH3Ejg20ZgGXXWEMWkPmebW3vkGHB73udrCa7mAPuBm4H3Aa+o6piqvg0c\nBdbOqZfh/AR4OPt/jGCmETUNqOrPge3Zj73AW0RQR5ZvAN8GTmQ/R1HHu4F2ETkgIr/N7pYWNR2b\nCXZ42wv8EthHnTVEMakX3dy6Xs5Ugqr+FMjfRjymqrnfZxgCujhfV+543VHVYVUdEpEk8BzBLDdS\nGnKo6oSIfB/4FvBDIqhDRO4BBlT1hbzDkdMBpAguTpsJlsKieD4WEkwsP8Y5DU311BDFpO60ufUF\nSv42uUmCGeNMXbnjFwQisgx4EXhGVX9EBDXkUNVPAO8kWF+/KK8oKjruAzaJyO+A9wA/AC7NK4+K\njiPAs6qaUdUjwCDQk1ceBR2DwAuqmlZVJXhGk5+s51xDFJP6nG1uXUNey67FAWwFXgb+BKwXkTYR\n6QJWEzxkqTsi0gMcAL6oqk9nD0dKA4CIfDz7UAuCWeIU8Oeo6VDVDaq6UVX7gL8BdwP7o6aD4OL0\nGICILCGYzR6ImI6DwBYRiWU1JIDf1FPDBb1sEcJeglnKHwjWd++tsz/V8HngKRGZBxwGnlPVSRF5\nnOAL0AR8WVVH6+lkHg8BC4CHRSS3tv5Z4PEIaQD4GfA9EXkJiAOfI/A9SucijKh9pwC+C+wRkYME\nb4rcB5wkQjpUdZ+IbCBI2k3AAwRv8dRNg/30rmEYRgMRxeUXwzAMIwRL6oZhGA2EJXXDMIwGwpK6\nYRhGA2FJ3TAMo4GwpG4YhtFAWFI3DMNoIP4P8Hbpim59Js4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a746e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"BloodPressure\"].values,color='purple')\n",
    "plt.title(\"BloodPressure\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JtyHe2afNYcXSRjp7UrR3pujoStHTN0DPkQF6+gbZ2/7WBWxbtx982/GV5THq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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8b8890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#2 小时血清胰岛素含量(μU/ ml)\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.Insulin.values, bins=30, kde=True)\n",
    "plt.xlabel('Insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AP2ua9tzAy9uUUpdqmvY8cDWwAXgJ+LpSqpbEgepMEoeow46dPXr7PdtY8u3L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\nIFQQItQFQRAqCBHqgiAIFcT/D6fj2OjhMssIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8b8a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"Insulin\"].values,color='purple')\n",
    "plt.title(\"Insulin\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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am2pK7sSl+TTWenE5Dem5i6Iq/b8cUXbODZmzZMplHXSn00HnsgBnB6PMzC1+0TMh8iHF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8c2910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#体重指数(体重，kg /(身高，m)^ 2)\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.BMI.values, bins=30, kde=True)\n",
    "plt.xlabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QtnwCQr5uBE8HEQ6E4B7rweRVU+OLbtwV5Zh623RM/KqAI+8f0i3AJBMKhCTb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8c28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"BMI\"].values,color='purple')\n",
    "plt.title(\"BMI\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DZDLuDJt0OkMqkyGRSDEwnGRwJMlO285oMk0ikWIkkWY0mWI0kSaRdBcXCwTc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\n+Nz/B0IKRHIoFPz2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2c1750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#糖尿病家族史\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.DiabetesPedigreeFunction.values, bins=30, kde=True)\n",
    "plt.xlabel('DiabetesPedigreeFunction', fontsize=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W9BKZQpNGznHcdAC/JoRckvD7fQDuAdAW/em7hBBiaAv7MelGJTIYDAagQZBzHLcMwG0A\naH40UwHcTgjZYXTD+jPhUJgVGs4wbJBkfJ1QzbXCcdz1AHYDeJEQcmHCtn0A9gIYBmAdIeRXahcM\nhXoiVqsl9Rb3Az74lw+w/antSb9P/8l0LHxyYR+0qP8QDoWx8Wcbsf+d/XB/5UbpiFKMv2Y8Fjy+\ngA2SjHwn9VwrhJA3OY4bKbP5VQBPA/AAeIvjuCsJIe8pne/s2YDaJWXJ98RAgKAp7n97P3Vb49p9\nmHzfBXmjQep5H9nSkOuXb04Kl97+1HZ0dgap4dL9oU8B7D5yiUzdQ1mZS3ZbyioKx3EmAE8SQk4T\nQoIA1gGoS/V8XxcCrX64m9zUbf2xKopoRnpl1vN4+aJVeGXW86hfvhnhUNjwa+VKuDSDkW3SmWuW\nAGjgOM4ZFeqXAmC2chXsQx0oHVFK3dYfq6KICYV8TR4g3FskeduKTwy/Vq6ESzMY2Ua3IOc4binH\ncfcSQtwAHgCwGcAWAHsJIeuNbmB/w2a3Yfw146nb+ltVlGxryLkSLs1gZBtN7oeEkKMALoz+vVry\n+4sAXsxIy/oxCx5fgM7OYL+vbqNFQzYyl7gYLi21kYvk4iDJPGv6hv743FlkZx+gp9BwPtMXxXXz\noQRcYiEC5n6aHfrzc2eCvA/p79Vt+kJDzodBMrEQgbhuAGSvEMHXkf783PNuGOIDPNxHOpgHQp4w\nY8UcTL63Dq6qEpgsJriqSjD53rqMa8h9GS6tBPOs6Rv6+3PPG408HArjg3/5AHvX7ut306L+TD5o\nyNkk2+sGDIH+/tzzRgJuW/EJtj+1PStubAzjyVUNOdswz5q+ob8/97wQ5P19WsT4+pCJQgR8gMeZ\nQ2fYd6BArhSAyBR5YVrp79MixtcLozxr+rMXRibIB4+mVMkLQd4Xbmx9RX/0cWXEY9S6QX/2wsgE\n/Xm9Ji+G7f4+LQIyn5OEefvkHumsGzBzY+r0x/WavNDIAWFaVFxcgMa1+/rdtAjInHbFpt/9E2Zu\nZEjJG0Futpqx8MmFmHzfBf1uWqSmXU29bzqCnmBK98ym3/2Tr5O5kaFO3qlk/XFapKRdeZs8eP3S\nl1Iyt7Dpd/8l2+ZGZprLbfJGI+/PKGlXAOA/4QOgX5tm0+/+TTa8MJhpLj9ggjwHUMpJQuPI+4cw\n/YGZqlpXLk6/mVeOcUi9MIpCQJcVhj/TbJrmYv7wMvfB+o48TJDnCInalf0cR0wTT0SrNp1LaV2Z\nZpc5bHYbBmWgvJiaaU6LMqEFtb5B2z5yfg3OvacOzuEuJtTBBHnOkOjjWlBSgDcWrE5bm86VIAi2\n6Jp/ZMs0p9Y3aNsbVu1Cw6pdcFYxhQBggjznkKa2NUKb7usgCD7Aw3PMjcPrD1K3G6nZfd2Qmhoy\nQTZMc1o8tuS2A0whEGGCPIcxUpvOdu7zuOlwsweI0Pfry0VXNZtsrkIzNUy6bgLq7r/IUK00G6Y5\nNa2/vfG07HYpX3eFgAnyHKavtel0SJwOy9EXi675bq+nmRq2P7UdnZ1Bw7XSTJvm1LT+wROHKHp0\niXzdvbCYIM8D8q2SkNJ0OZG+SLGQz/b6bC1AimRamVDT+osH2zV5dCkpBF8HbxcmyCl8HV58JlGa\nLgMAzICroqRPFl2zLQiNpq9iAzKpTKhp/dLtXhnNnKYQ5PvMSw9MkEv4Or34TKI4Xa5wYfHqa1FS\nXdonAjPfg6RyMTYgXdT84aXbfS1e7Hl2J4799YiqqSefZ156YYJcQj6++FycPShNl0ctHoPBE4b0\nQasE8l0Q2uw2FJUWwteUvK2wtDBn+kAqqPnD2+w2DBw7CLN/PU+13/MBHodkPKUOrz+Y8zMvvTA1\nM0q+5SXJdNpbkVRzbPRV0WU18j0lMh/g0dnRRd3W1dGVc/00U6jlXAq0+uFvpg8IvmZh5tWfYBp5\nlHybcmd69pCumSmXPW5yJUgqFQKtfviP0wWU/4RPtp8aNXPLxRkgjYKSApgsJkR6kv1eTRYTCkoK\n+qBVmYMJ8ij5NOXOxoKdUQNFKotkmRYW2chRkin09lOj1n3ybf0o6AlShTgARHoiCHqCKB5sz3Kr\nMkfuvYE+Ip+m3FpmD+mgx8xkZHrTbJmLRGx2GwaNHpRT71YNvf1UHJB9TR4g3Dsgb1vxia7rGnWe\nbGEf6oCzqoS6zVVVklOKmREwQS4hV+26iYhaGQ0jZg9aBopwKIwP/uUDQ4VuvgmLvoLWT6f/ZHpS\nP+1sD+DQu19Sz6Fn3Sff1o+A/FLMjICZViTksl1XSqZDp7VM34220ee7f3c2ofXT4dWDYt4eohnk\n4LtfIpBmBk0g/9aPRPJ5LUQvTJBTyIdISq2dNBV7s9pAAcBwoZuvwqIvkeunWtIj6Jm55dP6kZR8\nUcyMgAnyPEWtk6a7OKU0UHibPIYL3XwVFplG70CsNT2CnplbLuW1TwVxwBPXc/qjQGeCPM/RqpXp\nNX0oDRSZELr5LiyMJtWBWC09gqPcidFXjdVtXshnM0W+edykQr8Q5Pni25otjLQ30waKTAndfBYW\nRpPqQKw0yDrKnbjpo1tTcrvTYqbI1e8wHyO29aJJkHMcNx3ArwkhlyT8fhWAhwCEAKwihPzR8BYq\n8HUYaVMhG/bmGSvmoLi4AI1r9xkmdL9ONk0l0hmIlQbZ0VeNTdt3mjaw5/J3+HVZRFcV5BzHLQNw\nGwB/wu82AE8AmBbdtpXjuL8QQloz0VAaX4eRNhWyYW82W81Y+ORCTL7vAsOFrlRY5KqWl0nSHYiz\nPbPJ5e/w67KIrmW4PATgOsrvEwAcJIScJYQEAdQDmG1k45TIR9/WbJFNH1q5nBfpBgplOzjIyMCm\ndEk3TkCc2SzZcgeWbrsLS7bcgZkr52ZEO8717zDTMRdqZKtfqWrkhJA3OY4bSdlUAsAt+bcXQKna\n+QYOtMNqtWhuYCJlZS4AwJlDZxRH2qIQMCi6by5SluG2XfP0lSguLgB5h8Dd5EZpVSm4azgseHxB\nSh80H+DhPeGFqzy+annifYRDYWz82Ubsf2c/3F+5UTqiFOOvGa/7uh/8ywdULa+4uAALn1you/1y\nhENh7Hh4a9rtNZpJ103A9qe2J/0+8boJGF49iHoMtU/J7GsURnyH0r4FGP9tyD1Lx2A7hlUMyMh7\nHjzQYch3oJV0Fjs9AKRP3AWgQ+2gs2cDKV+wTJLikrdC0XzQZYVsOsy+pkwhVaeRTH3w4iTTR/tZ\nfeH7SvbPoeWlSfdRv3xznAB2H3XrLkPGB3jsXbuPuq1x7T5Mvu8Cw2YVOx7eGveRp9LeTFB3/0Xo\n7AwmmUfq7r+I2ney1acSSec7NLL2qJIJru7+i3Bo02GcbmiL+/3kFyfxzg/eM/w9l5W58M4P3kv7\nO6CdV450hoZ9AMZyHDeI47gCCGaVv6VxPl3kUghutqZPqVxHLd2nGnrC5o2aZmc6l4wIH+Cx/+39\n1G19bRaQmkdu+ug2XPHSNzH9gZl9vniYSDrfIa1vbX9qu66UDFpMcD3BHnS5u6nHZ+I994W5SbdG\nznHcUgBOQsgzHMf9FMAGCAPCKkJIi9ENVKKv3dWytVrfV14BejukUQtL2QoOCrT64W5yU7flwkJY\nOBTG9kfqc9IbREoq36FR3iRaFlqzveDpPeHN+gKrJkFOCDkK4MLo36slv78L4F1DW6SDvnZXy9Zq\nfV95Bah9AN4TXqCk93kbJYD1+qmn6tliH+pA6YhSuI8mC3Oxvdn0mkm8Vi57g0hJ5Ts0QrhqHQyy\nHTXsKndlPUo5d4b1NEjXfJAK2Zo+9aVXgNqKv7g4JWKUuYsP8Jh0xxTU3jVFMRNlup4tNrsN468Z\nT9028vJR2P5IfVa8Zmj38ekvNuWEN4gec56e79AIbxKtJrhsm2H7wuzbLyI7+wKlTuRt8aB1xwkM\nnVoe2zdVja6v/WArZlSCvNaY9HusQ/rjy46lY+6imZBGzq/BuffUwTnclfT8jNBYFzy+gLqoGAlH\nsqYN0+6jYdUu2f2z8d4zbc4zIjpYj6adbTNstq9nikToVTQyRVubN+UL9tXKPA0+wOOVWc9TO5HJ\nYkIkHIHVUQATIuADvKq3RyrXcVWVYMmWOwwZ4aXTekuBpfcjbvbE7iPUGYrrkEr3kYpJItHjRWTy\nvXVJwtOo5yL2KWl7AWTlmQMa+hGlyg2tDUZ/G1rfRTqmJ3GwkAq7iTq9VvT0mXTbq5U47zoDr1dW\n5jLJbWMaeYooaRTixxfyBWO/iRpdOBTG9X/6piHXMWKaRtO8ikoL41y1xPvglkzE7Efnabqm3lTA\nehe/jJ6pSNvrPtKRtVmQ0n3IlSrLtFeWlncRN9inqLGr5VXXgl7NN9spqrN1vX4vyDM5Aks7kbfF\nA5OJrkFJ2fv8bhQV2XD+cu2uZJmcptGm9b4m+r7HtzanfT059ArmTC5gZXNxTPFalS6MnD8Kx/56\nJKteWVrexZ4/7TTM9JSOsOtrh4dcod8K8my47Ek7UeuOE/jLjWtUj4n0RPCP3/4DwVCP5g6fqc6q\nNXe1SCZts3qFp9JMpXp+ja7nkzjYq53byOx/StcauWAUZj86L+v5ZtTeRUFJQc4losqHYjCZpN8K\n8my6btnsNgydWi7b+Wmk0uGN7qxquasTUdJG0xU2Wk1I0uuImunh9Qfha/bGbMpHPzwCs3Wz6qAt\n1h3du3Zf0mCfOAtylDtRNKAIRz88gobndsXtC8CQIh5K95Ftc4DSuwh6gl+LRFT5RL8U5H2RulKp\n89PIhQ6vpHnRoNlmjZz5KJmQlK4T5sNo+POumFlL66CtNthLZ0G7fr8DDX/eRd0XgCFFPFK9j0yg\n9C56gj2y/cZabEPR4OKstjWTM5Z8yb7ZLwV5X7nsiQJn7/O7VW3luVC+TGnwMVvNMZ9pm9OG8Usm\nUW2zRs58lExIid4J0sXjo389Qj2f0qCtdbAXA0rkrnF43UFAxpdAj9LAB3jZa+xbvRfTls1AYUmh\n6nmMQuldmK1m2X7D+4L4/LFtWRl4Mmk+zeUc6zRyr0UG0FepK81WM2Y/Og+Tbp+sum+ulC+bsWIO\nJt9bFwu8sTkLACAu8IX38TCZTUkdWEkYHl53MOWglcTAEjWhm0peFj35XBT3PeGF7zjdy0JPXhil\na/C+IOof3KzpPEYjF+QzbdkM2Jz0/putgCW5PED1y9N/VnpyDOUC/VKQ93VCrZkPz40JR5gBq8MW\nJwQLXAWIhCOyUYLZzI2dmJypcABd66N9nIo5JVq8+PT+TYZEQioJOX+rD46hTuo2pUFbz2CvuG+5\nC87h9Kx0eivVO8rls9u1bG3KWH9Ipb91tXfK7m9kYjM5lAb3vc/vTqvvpRNNzQd4nDl0JusJ1/ql\naQXo24RaidPSRPtq0BvEnme/gMlsipuC9uV0zma3wVpkVdUupSYppZwSAEBea0RhaWHa02x1L4pC\n+E/4krYpDdp6/POV9h21eAwApO3nb7PbUDmrCuTV5ChaAPCf8BluEkynvynWBh3mNHzWm2irVvO/\nb/jzLpht5pT6Xiqm2b42xVhWrFiR8YtICQSCKV/Q4ShEIBBU3xGAyWzCiEtrMPG2czF+ySTU/Wga\nai4fDZNZNjjKcCw2CyyFFtT/8mMEPclpNAOnAph427mw2IRCG1sf+hi7n9kp7BsBgp5utO44iaC3\nGyMurVG8Fh/g4Wv2wlJoiZ1Pd3sLLTiwZj+1ra7KEtT9aFrcuV0DitG6vw2tO07KnjPxHrUivR+b\n3QZvk5t6nZIRpWjfezrp9yG1Zbj8T1cpvu/K2dWwhCLwHPeC9wfhqizB+CUTMWPFnKTjKmdXI+jt\nRuBUIGnfqktGym7T098qLq5Cw6ovEA4ma5K05y9F67chfa6f/deWlPubxWaB+/BZnPoiubJjJBJB\nd0cXKmdX6/7eEu8jHApj60MfY8sDm7Hjic9wYM1+eJvcqJ4/Cl+uJdS+KpJq39P7HQDpfbtacTgK\n/0NuW7/VyEX62r9ULSeLOLp3e7qxb3UDdT+lRTMjNYFUokhnrJiDbk+3rCapd3FZ7n4u+MXFaK5v\nwtn97YiEIzBZTBjEDUZXRxf1PN3ubvQEexSfQWLd0YKSAgQ9waTjRG1w+gMzZX35tfj5q3lAFJYU\nYsLS2oxI4juIAAAgAElEQVRE8SY+V0e5C0E3/dmpLdLGQus/PEzdzvt4w7xt5BbTw6GwbB6g2L4p\nOjakkn2zr/3q+70gN4p0UqXKTUFNJhN2/X4HZj48F/XLN4P3KdscaR3SaH95vSYpcYG3pb4JvuZk\ns4zexWW5+zmwZh+6zvQKnkhPBO2NyZp47DgdH7GlwII9f9qZNHhcuHwWPlu5RfMgKac0hENh1D+4\nGUc+OAR/q0/xPEaZBNVS4vpb5MPg1Z5d4rnkSFeIqdnBI+EILA4begI8QHESS8exQc978B33ypoX\ns+VmzAS5CulqvGo5WRr+vAswAc1bZOLiATjK6TbHTGgCqUSR2uw2jLpiTNqaJB/gcXj9Qeo2qRCX\nIpdYSs9HLDd4HN/WHJdzJpVBMhwKY82ClzWfJ90oXrkMkkc/pLs20lAL/NIaDZyuENOSh6bHL7+o\nmM4sRs972POs/KAmfZaZ9EnPG0EeWw22Ii+KRyRGICr5lx95X9DU5Bg+o5J6z5n0l9drkkpXkwyH\nwvj4Xz+kavVKqCWWon080t+UBFP7PrrGr2eQrF++OalWpJbzpGoS1JsSl4bSs9MTDZzJnDeJ2JwF\nKBwgLHob6dig9h74AK84SFZfVgNLgQX1yzdndCE05wV5X64Gp6LxyrX33Hvq0PAc/YMSXehonhcA\nYCumC4xsVz5RQq8mmZg2N1Fr1Yp9mAOjFo1JSix14fJZSR9PzeWjAABHNhyO/Tb60hrd2QeNqGAD\nAF6Dp91K15ObudAEIO3Zid+cHuGarl3fUmBBUWmhbBI3KaFOHle/eQN4XxCDJw5B8WB7ytdVQ9p3\n1Qa2c++py0q6kJwX5Jl6CFqmOalovEqLM3IfgKuiBFWXVKPxxT3Ua3310VHwAT6pnUodva8CjtQ0\nGNpAV+gqULR3KzFq0RjM/nVyYilaJOieZ7+IO9bX5MGu53fB5rRR1yfSNdv4jnsRkBmcAcBxjsPQ\ncnKppMSdsHRS0uD76f2bZNMRzFw5V9ZUaHPaknLW60X0abcPdWD7I/WaB3drsQ0bvv1uNC+OC5Wz\nqjBz5VxDo2HlzFay33VVCYoGFWVlITSnBbla5GAqD0GPhq9X41Vq77EPj2DkZTVxH4hIzaLRmHTn\nFFlBLh00pB+9XEcfUluWtQLUgD7bHzVtrsr5B4wbhI4DZ5J+H1JbhpkPC4O5dABR8gCiQ3eRGzxh\nCPX5ah0klWyngJDd0MjiyqmmxDVbzSitGYBwKIxPf7EJe1/YTT2/KHimLZuBbnc3WrY2xWny05bN\nQFd7Z1pFJo5tPAz3V25FrxoavC8IPpo339/iBXm1EYff+xITltYaNnuXM1sNqS2TVaaylWAspwW5\nokYcjRyc+8QCzS+JD/D49P5NcS5LShq+XjckNVfDkdFo0+bNx+BucicnIqqSHzSKBhfHTXeVOroW\n1zsjEGtNygmiRAGvN20uIJhOrl9/Mz5/bFss77tjqBM1i0Zj5sq51LQBH/1kg6wHEA0+EAT3rYk4\nvq057vwzVszBZyu3pGT3V7OdFg4qgtlm1jXbVBswFQOXrhiDmSvnKp5j24pPFO3p3hYPPr1/E1q2\nNcf64LgbJ8RpvuL/ae9eqe16vGqSMIHqtWKkG6RS3+3q6ELtXVNig2RpVSlGLKhRTTBmpPkzpwW5\nmj2OvNYIm8OGKd+dqqgFiKP9oXUHZTuI3DRHzyKefagDzuEu6oKdyWTCe0vWwllRgvFXjcPYW2vj\n6lAqJSKqWTQanz+2zTD3MaPY+LONVEEUCUdgMpuSBPykO6foSpsLCKaTwpJCVfu7lncsh6uiBDMf\nnovtD9fHXASFFLJmzFgxJyUPkkCrH75m+Xs1WU1ofIU+azi8Pn62qWcWqdZf5UxfWgZZm70gTgkS\nNV/x/dDa6hjuQvGAInS5u2XbnsoAH4dK8UgjTBiKaSJO+DDle1Nx0b/PRqDVj+raYeiI1rJV+66N\nMn/mtCDXkhq24c+7kvJDJ3ZuLX6vcsJP6yJeOBTG9kfq0dVBjzSTpiaVKywh9xFOWzYDr819UbH9\nUsSRPtPpPfe/vZ+6bf+rjbFpLqBtnYCG1HQCKNvftfo20xAHSiW7sPS6Wp6rfagD9mFOWRt516lO\n2fb4muP7op51olTdF7V5o9AlplRQJmnWzV74JYoNre168+InIreWEbumAYqNFjNrXIIxSVHybKQL\nyfmkWTNWzAG3ZKL8DhEoZifTOtqrTXOkQoSWYEjswNI6nQAUU5wmnkOawGrptruwZMsdmLlyLrra\nO3V19JGXC7bXV2Y9j5cvWoVXZj2P+uWbDUlgJRJo9cPd5KZu4xOfQZSjGw5j5GXawpXHXj8eN2y8\nRZN5SOs7ttitGHPdeDgrXTBZTHBVlWD6T6Zj2rIZmpIkiaYkLc9VKXEbANl+IbLz/32OcCiccgIn\nuayFciglBjNZTBh7/XjVJFl6NGtp25WubXMWxN6XmJkzkcEThiheyygTRsWMSurvapq13HdtpOkz\n5wW5GDnoGE7PcJdIYudWm+KKqL0MpY9YsQPLKApKGeKkHyEf4BHqCslm2JN2dFdVCSbfWwcA1BSc\nm3+60bCsbPahDpSOKNV1jK/Fi6AviHPvOQ/OCvlMf84KFy75n/maO7pWjc5kNuHg2/sRiQBjruWw\n8LmrMe+ReYoDpfQ96U1tOnPlXAypLaM3RsUc0PjiHmxb8YmudLvpoDTwTLilFt/48QWqWR71aNbS\ntitee+kk3Fx/J5Zuuwu3f/GduJTLYn+/bv3NmHxvnaygl/u2tWR9lH735PVGWJ0FsDltcdfXqlnr\nHVz1kNOmFRGb3YbK2SNk83lISZxG2Yc6YLHbZCPATBYTJt0+WfVlKE1vz/12ne6poZqWkGhrtNrp\nnTTRfQwAXpn1PHVf8mojWuqbMOqKMXEeBgBSqjU5/prx2P7Udk37ixx4Yx8m31uHm7fembTwLDJq\n8RjD0iBICUUXQP0tXny5Zj++XLMfpSNLUXXpSNnj7ec4UFBSkFJMgdlqxg0bb+kNzz/lh2u4C9Xz\na4QgMAXXRPG8U++bnrVYgURvFLG83Vebj6HxpT2yfVAUlHp8zBPbLn5/X208EucIMG3ZjLi+KWc2\nmrlyLqYtm4H6BzcnedMkftta1xxozhHijJtbMhGzH52XEzUFgDwR5IDwoo6sO4igVznDG61zmyLy\n6k8kEsGU701V1P7UPmKlj00OtRlA4sAhdiCar67oPgYIZh+lQcXX7MXuZ3Zi3+oG8H4eVkcBTIiA\nD/C63d8WPL4AnZ3BmDeJyaRsqxQRBd+c/74M7Xvb0L7vNCI9QiKswROG4MLls3TZ9212m6xrpxru\no264FVzI/Cd8eGPBalRcXJmSG5nZasbsX8+LLYQVDS7G549tQ7dKPxbPG/QEM75YRkuoNe7GCbAU\nWdH4XK8rolIfBPSVO0xsu2h+GPDEIhxrOBl7Tq/NfTFJ2MqtlRSWFGLebxbq9pBJtNtrWThvUUip\n0RfkjSAvLClE3d11qhpgYgfxHfciFAjJ7u8Y6kRBSUEsHLukulSXW6Hax5aIzWnDN779DdTdf5Hs\nPkoDR2FpEa5bdy21nYB27VR0zwtRFiUBfbUmpz8wE607TuAvN65RPQboFXx7/rQzzk870hPB6YY2\nrL3iFUUvBymJmfjEhS9nhQvd7i7NbohSFzJvwrPzNXkUZ4NaNGNR+CQGKikhnjfTi2U01z/yaqNs\nBSClPihXtLrb3a2p7XLPychAQLWZ1fZH6tWdI1q8+GTZX3Hpk5fnROm3vBHkQLIGaLMXAIgoRpOp\nBWUUDCjEi9NWxWkb45dMwsX/eUnsBSkJR3HqLe3AStppYWkR5j0yL+aeREPR1emkD9Yiq2LBBK1h\nzXIcfOcApt43XXOYs81uw9Cp5bqm1QUlBbIfFC3BVDgUxuxH5yXtmyiExGdeflEFvlxL96qh4Wvx\nYtSVY1H3o/OxdvFrqmYPKVo1Y71udtLzypkURO1zgKNI83m1tkluEFTqg3JeM0pasnSbWpvSTems\nppR5jrk1v6MDr+9D0YCilBOcGUleCXJaJwHk7btagjLO7muPP8bHY8+zXyASjsT5p8tp3OLUe1TU\nnjfhlnPha/Fi3a1vUa/pP+mD94QXKJHv3OnkUOEDPDplcnRrJdDqx4tTn8WEpbVxA5r0GoFWPxwm\nSyycWu+0Winijcbe53cDEaGMnhb/4xN/a5H16ZfjLzeugX2ovMtgIlrXV0TUFgMd5U4ETvlllRKp\nSSFRaJWOKEX1glG6oxhTcf3TOgMRF0ClrnlSuj3dMZu277gXzooSTLpuAkZcPVZTDv9EtLhpqn1b\nAHQ9j8bVDTi8/mCs/aMWjcY1T1+p+XijyCtBLs3DIH2Rcv6hap3UrFA5ZO/zu+P80y9cPispramI\n2GH2rW4AH+DhKBcCfWgajXO4C44yB9b/fIOs5qA1olQuO51fplybHkKBUFI5upjtcP1B+Ju9vWaM\nqt5nBGibVitFvNGgle9Sm7mMu2GCYuGB+AsI/2kV4oDy+grt3SgJEVdVCW7YuBRBTzClVAfuo+6U\nTA9KbbI5C6iupFo8vOQ0455gD3wtXuz6/Q4ceHNfnNnT1+TB9qe24x/P7gBkPGWlOfx7gj1xCp3c\noL5v9V5MWzYDhSWFqt9WSXWprn4Z8vHwRb9zUQ4UFxdg6oMXazreKPJCkCfmYdC6KKdYV7DcCf9J\n+Y9WGsAjTu273PJlpYDeqahSZGHNotHY/JC6/U/JLqr0oejxHNCCNMpQzoyR2H7atNpzTPA5L6ku\nhdlqVox4U0I6tVbTrmY+PBeFpYWxZ1g8xG5oUWDH0OQ88UrvRk2IFA+2azJnGZWHXhxs5BaKxy+Z\nCJPZpNs2r5TfvbOjKy5AiEaPX35NSxzQT35+PG4dRWkxmvcFUf/gZsz7zUIAyt9Wqv1SCnmHYPJ9\nF2TVzGKKKHh0ZIK2Nq/uC8otEE2+t041J7ici1vt3VNwZMNhzeHcjnKnkDNcR0wNLUXotGUzsGbe\nS3AfSw6mcVa4cPPWO+M6AE2zU3seehbUVDEDt/ztbhQNLsYL5z2juHjoqirBki13xLVfSbABwkcv\n/aAKSwsVM96ZLCYs3XZXbBampW+Iz7CgpABvLFiteZCzD3MgcFJe8NfeNQWzfx1vt1drT2xxVpy1\nDHOiQmemPveRDrx80SpqXzRZTLjpo9tgLbIqRiHTwuhpMydazhwl+ACPV2Y9b5gioQerw4qQzCDg\nrHTh5nr5bwvoNdFaCixx78habJMNcqOR2EeNoqzMJRtGlvMaedo5wZs9sDpsACIIdfXAldBJtQo8\ntZzhNEKdPK5btyTuo3If6ZCNiKQlAku0LWp5HmqFLPQg2kM//cUmVQ8Qmv1SzW6ZqMFbCix447KX\nZNPa0vyPI+FIXFoAm9OGnmAPzh48E8tnI7ZJq7Ylmjq2rfiUqggkpg8AtPfVRJ9n8nojWrY2G1LB\n3lpsw7pb3oqz2SaeUy6MvvbuKdS8RXqKXKQbbp8WEflwWf8JX1LftNltcFWVyCoaYr8sGlyMvz+6\nNa6PWR1WmEwm6jdRWlWa1VoAgAZBznGcGcBvAUwB0A3gHkLIQcn2+wDcA0BUo75LCCFGNdCInOCh\naDCQ1W5F9fyaWMemCQG5TGquihJU6/RVdg53JbloiRGR7qN0YU5ea0RhaaHsTEPr85j96DwggpR8\nq6WMumIMAOVSdCJS+6WoyWkRbFJBwQd4RR/r6stqkvyPTWZTnMbE+3jsfW439j63O2a/FwOgpi2b\nEbu+77gXNruNGpsgmjrmPrEgZp5RyrzIB3i07jihua9+/tg2zVk4E1Ey0UjTudLOqZTi99iHR3DR\nQ7PTMgkYbdrTQ6iTh9Vupbobyy3QqikaYuroyd/5Bs7/1wsROBUQzlfhwltXv4YzFIWDu4bLuveK\nFo38mwCKCCEXcRx3IYD/AXCNZPtUALcTQnZkooFG5gQPBUJoWLUr5v1itpox65FLceHyWTEb7t7n\ndlGF3/CLKzH1p9PhP+XHkXX0upK0YxLREhGpZOfU8zxmPjwXZptZWFXXWUJNdMOcsWIOvE0e+E+o\nH5+4IJnKIKyl4ooUNZe+xIVoUeO6afNt6GrvRNX4c7D+5xtk7cBqSagSZ38mswkRiiaQWLsxXRt3\nop23pKIEgTMBqoYoPadSkW9viweeY27V3CVK6PFeMhpXpbyyRVugVXoPh979EnU/moadv/mcWpR7\n7RWvUIX4kNoyLHh8AdrPGrcWowUtgnwmgA8AgBDyGcdx5ydsnwrg3ziOGwZgHSHkV0onGzjQDqtV\n3luExqTrJlAF38TrJmB49aC4384cOqM6tftq4xEMeGIRAMB7wosB5a7YecbNqIajpAjkHYKOrzpQ\n4ChAJBIBebURB97YF4tApJksTBYTIpEIChxCKDN5vREnP2vB+GvGY8HjveaSBY8vQFdHF3Y9T9eW\nfce9KAoBg8rouS30PI9r/3A11v1gHf7x238oPhMptbfU4upnro51fJfNBle5C16N6wni8x1QO0x2\n9lFaVYrq2mFJH9cAR5HsMQNGDkDNecPjjtHyvoHehWipZ8HCJ4XFr2v/cDX4AA/vCS9c5a4kO6rY\nR2wJzxYAPviXD6gLwIlI341Sm9XevRRpu0OdIfxuyu8Uz+lyFOHE1mb5E4aB9297GxOunRDXX/Vy\nzdNXori4AOQdAneTGyUVJbA5bDgtUwMVAIZMGILqWdXY8UyyPnjOuecgcCYAX4tPMdPhxOuEdovf\nr7vJjdKqUnDXcNT7UXoP/hM+rL7wz+AlqT3EvtP62XHZdZyQj0dPsAdlGt6fkWgR5CUApF9VD8dx\nVkKIOH95FcDTADwA3uI47kpCyHtyJzt7NqC7kXX3X4TOzmBSHoa6+y9CW1u8cOGtUJ3adXzVgTfv\nfjuWID/Rljj5vgtQecVo7HhiO45+0Dtiix1IriNNumMyeD8fN2V2H3Vj+1Pb0dkZjE1vy8pcOP+X\nM7H3zcZY/g8pzuEudFmRdG+JzyNRi6Q+jwCP/e8ekH0WMcyC+UjURjv8XQi7AxL7oXaN3t3kxrGG\nkyitGYDqBaOo2tmIBTVCUBQlMErPMVreN43Gtfsw+b4LMLx6UO8zK7HFzq8luIQP8Ni7dh/1/CaL\nCREgtiYjfTdKbVZ791RKbBhY7lIMWvPxPM40nIRHZdDzfOVJ6q8iehY9pz54MWp/dH5sHcC9/zSs\nzgIgEkbIH4oJZEd5r6kKAKzFVuxd0wjfCWEhuHhQMQIdXfC1CGtTtG/PVVUSe8btZ/2Y+uDFmHzf\nBXFtTdSQw6Ew6h/5VAjck8lgxsvkZ2rd3Sp73x1NbnhPeNFTYrxpRWlw0CLIPQCkZzCLQpzjOBOA\nJwkh7ui/1wGoAyAryFMhMQ+DUkfSMrVLTJAvLYgAAPtebUxOR6uA2JGU8oYnTpk/f2wbVYgD2tNi\naoko07L45KxwYfHq5JDrVHN8S80IqYSX6z2mYkaldn/xKLHsexQtG9AWXOI55pavkRmJ4IqXvglX\nRUnM5VJEKTdMqvlTtAStKdWXTETaX1MtgJ64DiBNODXj32dTfeYXPrkwJoR3/X6H6hqPo9yJGzYu\nTXLbVFugVauGlCqOcxxwlbsUI7czgRZBvhXAVQBej9rIpYUlSwA0cBw3AYAfwKUAVhneyihaVs/5\nAI9Jd0xBmA+DvLFPxm2IPgInFkTQSvX8GsxcORdnD56R/UgS627K2eZszoLYgpwaWp6HlsWnUYvH\nJNlF06naIhVGiYNOQUkBgp6gYik6LQNVkmeSM5r8S2NuFaXoRKV737d6L6b+9ELs+L+f4dC6g7Lu\nqDZ7AT5dtinJewQANTeMqAxoLSNHey5xaSIo+WKU6ksmIu2vWgY1PWX9mj/5CoB8IJ8YI3D0r/JR\n2SKBU34EPUHN6STEtqZVkUiBmoVCOl5pxHM20CLI3wIwn+O4bRB8Ou7iOG4pACch5BmO4x4AsBmC\nR8smQsj6zDVXHprWMO4GIRn+8W3NMV/u4RdXgrxO195SEeKAsNrPP8Qr5nURc7IAgl1eTpMLdfLo\nau80rPq3kqZmc9pixWkTSSl0u9KFUVeMoZ7PUmDBnj/t1KXVKQ1Uctkhx900AQWOAoVBXEBJ81W6\nd94XxFtXv0YtBp24H817JMyH47RM0VQgKgNKqPnkm61mTH9gJsZePwHv3/EO1Qe+80wnxt0wHsc/\na4HvuFc2J5C0ypTSwuy0ZTPw+WPbdJX185/w4fVLX8Loq8bKvn+t/c9abEPR4GLV/VI5t14GTxoC\nk8WEpyc9rStw0QhUBTkhJAzgewk/75dsfxGA9jpkGaL+wc1Jpbr2PrcbtXdPweKXrwUgRBUCQMvW\nZkPdo8RkO0p5XaQ5Wa7478tVk3AZSVJGOg1BKDHNSqOGq5afWU6r6/Z0687rrChc1h/C0r/diaMf\nHqYKci35UexDHXCUu2SDxdyHzsoe6xjuRNDTTX1ue1/ag55OesCKqAzoSW0sHSCuefpKfHr/JiHv\n+UmfbOEK/3EfDqzdD0e5C9xNE5NS1YqIA51SWmRviwdbfrEJB9b0JieLK+unkOvGf8Kn6G6pPYtn\nENsfqacmVIvbTzJjMMJFUgz28x33wjHUier5NQh1CWktRNQSvhmJZcWKFRm9QCKBQDDlCzochQgE\n4j/OcCiMLQ98hL0v7KZ23rbdp9Dw3C4c++sRBE76UD2vBr4WD1p3nEza1+qwIszrL4fmqizB2OvG\nY+dv/q5Y+SXo6UbrjpPgO3m4RpZS28D7gjj4lwPwNrlRObta8JEO8PA1e2EptMCikB9GDpPZhBGX\n1mDibedi/JJJmPydOgz9RjkKSgplzxfmw9j52x0IB3sUz+2sdGH8kkmY/at5sBbS9YJuTzc2/eB9\nhIPJz7a9oQ0H1uyD+0gHSkYOgLXIqnqPvmYvdjzxGfVZh4M98HzlxqmdJ+nvwgTM//1iFEe1OFqf\nstgsaG9sQ7tchKncOzYDC36/GPtf20vdJ6LQt3h/EOOXTELRQHoWw872AOof/Jg6OPlb/dj94i4c\n2UAfvGjt571BtDe0Ydj55Rh2fjkCpwLg/UG4KkswfslEzFgxByazCZZCCw6s2Y+gh5KeIgKcIe3U\nez375VmEukLUdy4lcCqAibedG3vn4vuw2CzwNrmp30gip3a1wn/ChxHzamAy9wYF8QEenqNufP7Y\nNtT/8mPseOIzHFizH4GTPpRUl6LtC/lFSxGT1UQ1n9XeORkL/3w1uBsmIujpxtG/HsHJz1qo52jb\nfQqdbQFUzR0Z1z69OByF/yG3LecjO2lIR9ftj9QrLlrQ8oHILaZFwpG4EVVKychSBE75qcEGepPt\nkHcIrv/rLbE20OyZStXo9U7VxOdVNLhYs3kj0OpHyK8iFMzA4pevlfU7Fq/798e2KWr2vmYvGlbt\nQsOqXbEAHqV7VNOYT+08KasNOstdCHWFwAeUtd+ZK+fi8HtfUtst5wLnqijBOXVDU9L2HOVOasFs\n0Zxy8N0vZRN6+Zq9uuMERI5uOIwlW+6QXY9Qcx6Q8+DSaqZUKsqRmBpaNj1GWCiNd2rnSdywUfiu\nYiYomW9r3E0TNLUvEoq/P6k50mw1Y+/z9LiTuHNQEr4ZTV7kWhEZPNCBd37wXlwlk253l6z3Bw1p\nPhDaR7P1oY/jFj1FYUItsJvwUrXmOJHmYuhsD+D1S1+ihv7LZZ+TC6UG4gc5MWeEtFwczRun9u4p\nuOih2UmLVWo5MwaMHIBr31+S5H2QuBAJE3TlqAHU8+hs+vEHssUeTBaTbOZDm9MWFxh0zdNXygZv\nyL3PIbVlVD9isc2f3r9Jd0TtuBsnoGhgUdIgq6RcxDBD+fnKRCsD2vKCxN7nuoO63FC1kOgxVVbm\norrQeo65hdQDKgNW7V1TYLapp95wVrgAE3QPgNJ8SHrzytByEelBKddKXplW6n/5Mf75238I07zo\n9FBt6paIdAprsVli/wcEE0T1vBpM/k4dxl47Ht0dnWhvPB1zS0zEXubAwueujpkUKmdXo+tMJ/yt\nfvCBoDCNohw6oHoAzvvh+bDYLAi0+rHjqe2yZgIabbtOYfefduLAG/vgbfKgcnY1IuEItj70MbY8\nsDk2hTzwRiOOvH8o9rzkzndqZyt2/3GnkFp0zX54m9yonlcDz9EOnFKYfg4YOQBfPPPP2PVEc5Bo\ny41Nx1MYuhOn3IlUXFyFhlVfUN+/q7IEi164Bj1doZjJwOYoQDjYI+wf6TVzdXu6MXz2COo1KmdX\nI+jtjp3DOdyFmsVjcNlvr0A42JNkjrhw+SxB4G04BN4ThMkivH9nhQvhcFjWbGd12jCktgx7/7w7\n9q7E9p090K7exxWer32YA1e/eSOaPj6KoCd5ELc5CjD1p9NlzWJAr2muck419r6wK6X3KUckEsHu\nZ3fG+g+3aCw6u+IVM4vNAnuZHd4muklUiu+kDx2HzlLvVUrQG8TIhaPRvjc5SMlqlzex8gE+Jj+U\nTHzUY1XMZ2oomVbyRpDzAR6f/GITulVSyarhqixB3Y+mKdphTSYTdjzxGcjr+xRfkvSlxrLabTwM\n/3Ev7MOccFWWoLMtOQBqyh1TUDlvJAAo2yDliObODnqCgjByd6H5k2O9wjMqCMS8EFoI8/EC7vD7\nB9G255RgWjAj6TmYrWZ4j3uTBE/X2U4c2XBY3/1QUOr0fIBHZ1sAoc4QdaAZv2QiRi0aE1sXGPNN\nDofXf0n9uD3NHoy7cQJVSxIF2PibJwkFQZo8OPFZCw6+fQADxw7Cgj9eiQlLa1H3o2mouXw06h/c\njD1/+gK8eJ3oMxt34wScc94wWSE0cMwgNH18TGYwlxfijnInuJsmIHC6U/Z5czdOxKTbJssKwXCw\nBz1dIYy4tEb2OuIaTdGgIhx85wD1WiaLCTCb4BzuQiQSobbb5iyA/Rw7eD+f8sDadbYTp3a1yn6X\nvDDQFMEAABu4SURBVI9XresrMuz84SifPjxpoL7ihWtw6L0vqfcplR96v13HMCcm31uXskbeL2zk\ngVa/bNZAPWgJuNAaLCCWLHMf6UgKXgic8CFwwochtWVx6UGr59fg/O+fj2DURqvmHqjFa6Rx9R6Y\nzMa6N8XlkaDIEpqpCRDsmf5W7Rki5aD5edPSryY+30RfbJvdBmuRFT6ZYhveFm/MFU5MrJVoslJL\ncNXt6caH31+Pg2/Tc8Ud3XgY1fNqYHHY0COJFrQ5bSipLqVqhWo4yp246aNbUTzYDrOVbgIqGlSE\nGf8hPItpy2YI+WZUcrFIobk7ypURnHBLLcZcw2HwxCHY8cR2ansmLJ2E6Q/MjJlJaGZDpVzeZqsZ\nsx+dhzAfRuOLe5K2x9CoIX/10VEs2XIHpt43He2NpzF44pCYP7paYRe1XO40/Cd8eGP+yzEXXSNd\nEvNCkIdDYez6ww7FcFo1pPZstfqBWoMFeH9QyG8dTZZEo9vdjRs2LkXXmS7seXYnjn54JK7y0IwV\nc1JafJXSE+gBoOxdki2U0v2KYetOSeWgxIVeEdqAqzf9qoiau5noCpeYWEusaCPXHw6vP4gwH8b+\nN/YpRgL7mr1CqboExl43Hl9tPiZ7nBKjrxobEzozVszBqb+fwMkv4jXurjNd+GzlFsxcORdd7Z3g\nA3SlQGsWUV+TB74mxA2eYhWorzYfQ+NLe+CsKEHN5aNw7j3n4eiGw9T85uI1abib3LKLnyIX/fts\n7H9lr6wyoRVviwef3r+JmqpD7pu8cPks1C/fTFUm5PqyFF+zN+bIMOuRS9Nqv5S8EORGhNMWlhZh\n2rIZSRrGyPk1OPeeuljeaqWw60S6znQBZ4RQXLnVe99xL4KeYNLqtlpebnHxVazQ4m32GGqbzBTO\n4S6MnD+KqqVMumNynMDtbA+gbc8pHH73S3y1+Wjsg6EFFSkNsGrpV7Vm5EtMrNXt6cZ53z9fPsFV\ns1eTNibn5XLswyOaZi9Whw0FrkIE2vxx+fRFeoI96DzbST1W1LaNzCLadbYLN/71FgQ9waSZqK/J\ngz3PfoHJ99ZhyZY74go3uI90YM+zO3Fk42HZxVktuby72jsRDmsX4nLPXy5VByD/TSYugEuViXO/\nXYc9z+5E8+ZjcDe5YT/HgUBbABHKgLP/1b24cPkswyI/c16QGxVO6z/pQ/2Dm5NenOj2VjzMgeJB\nxeg626Xbw0IJtYrxcnm5gfhQdc8xN95c/IouD52+oOLiqlj6XLlSWuFQuFerafbA6hCSKSECyDlR\neY65ZTVqb5N8QV4RUfAdevdLzcVByKuNaP70K9nAKKVMfFLk9tFarCTk5xEK8Cge6sCw6cMxbdmM\nuGm5ktnR2+JB644TGDq1XHYwq55fkzRDVVJofC1ebFvxKWY+PFc2jF7s13GFGzRorKMXjlbNH6Q3\noGf0VWNx8G1a4jj6exHbDiCWViLQ6ofZZsLBv9AT0B3deBgXPTQbs389DwMcRTjWcBJdHZ148/JX\nqPvzPj7tlMFSct79UKmslR5SdTdKl8n31mHSnVPwyozn6DuYgavfuAFDp5arjs5bHvhIk6klEavD\nhpGXj8bBtfvVd04Dm7MAt3/xnVi0qJwJS4ubZmJpNCU/agCYeNu5mP3reap2RyV3z0wwcPxg8H5e\ntuCy3mIlQLLbKx/g8folL1BT/4qplR3lLlRcXAlLkRUH15I491pLkQW8n4d9mBM1l4+C2WrG4fcP\nqZZB5JZMFNJdyJScW7rtLk2Jr+LuzWED38mrxkzoKWdYfE4xigbb4Wvy9lb4kSlAAQAwA+OuG4/m\n+iYhzYHo3qni5nnL9rtRWjMg5kLZvu80Xpvzguz+3/rkdl2CPK/dD1Py6qBQs3gMTnzWkh3zhMUU\nFyH3j8f/hlM76W58JrMJ5PXGOPc9OXt71RzBHc5/0o+gPwj7OQ7ZVJtSwnwYFRdXYsikMrQ1tKX0\nDAZygxDqVo7UG3sdh5ELRsNis8gKcT7AY8sDm1Xfp+h++Nl/bcHuZ3aqBpi07T6FoLdb0fsCEMws\ncpG9WhEFaWe7vLeIiLXQiuEXVlBL141fMhEX/9clwjtt9au6zImEg2G07jgJb4sHVXOqhRnDqU60\nbKdEFkY9nHhvEO17T6N9bxt6unrXUyLhXg8T3hdE2xetOPXPk+A1eH50n+1CQUkhtd2Ocic6TwfQ\n+NIeXf0t0Xsq8Z2KHjQVM6vQ+OJuTe7HIX8IXac749xvlSK4TRYz2hvaemdhYvtV7uMbP5mGAmcB\nHI5CuE/74W/148u1+6jXsjkLMP2Bi3VFait5reS8Rg7oG30TERM5iSlms1GC6uo3ezVsvUEDtXdP\n0ZQ3wnPMjVBXCBu+/a6mWUYsN4TeGUlUC7HYbeiRWSwDohpOZwhF59hR5CpEqDME34nkupFaZ1hi\nEeF1t76ddsCF+LwAId+OpcCCzT/dKBtQpIXJ99YlJcCSxQRYHUJ2xlBnKK4Qt+gl87f/+DSlsnxi\n/174q/l45/+8h+b6r+Bvyc5sQynwymQ1U23DehG9cwpLi+LWt+xDnYoztL5g7PXjMe83C/HPX23D\nP/+8U9EMeu495+le7Mzr4stAr33zq41H0HGsQ/hRw3CQmMgp3RJUJqsZlkKzbKVuALDYrRg4blDs\nmnozrTWs2oVQsAcXr5iT5ArHB3j4WrxR7xfBI8CqcbFEmo1PF9FvUUmIA4hNU7taA+hq7fVfT1xA\n0mrfdA4XUuD7mrU/u0Tvi25PN7b820c4/P7B2EdldVgxavFYXPTLWWipb0rZ1LZv9V4UlEaTm6lF\nVkbic3Ff+OBMfLayHq9d8kLU88OFoDu1/NWiF8T+V/Yi6A/CWpy9WpHO4S7MfHguCksL0bi6IU5w\nGSHEgd5MicWDi+OiaY0U4vZhTgRO+WQzQWrl4NsE7Y2ncUahEpKzwoVRi+kZQtMhLzRykQGOIrx5\n99vqRQRMwKDxg7HgD4vhGtFbLKHb040XzntGc0a/VBGn3mKkXyrV7M1WM8LhcMydCwCObDisrp0q\nhGP3JVJt+dNfbFL1Qpp8bx2mPzATqyb9Ps73WglnpQs3198pLKY+uBmH1n0pqxXZnDaUjhwgW7Ir\nU5itZpgLLbGC4PmMuI7BB3j8ufb3Ob8QL4fFYYVzmBPuQx0Zv9b4W2txyWOXpeRDnvcauZSWbQo1\nB0UiwJl97Xh19gtxSZiUfGmNhPfx2P3MThzf1pyyoBB9ZEV3Ls3oFOKOcqfgAmegpw4NqbZ87j11\nioJ87PXjMWPFHHS7u4Ae7Q0bflEltj9Sj8bVe1UrPPE+Hqcb2jDsvGHwtvqytvgZDoXT9n/uK2xO\nW5xpSNQqPcfceSvEAaDHH8qKEAeA/S81oMBuMzx5Vl4Jcu8Jr66pNhA/tZ/+wMy08xDroV1hipVJ\nRHu4/4QPjmFOdLu7qLMQZ4ULl6+6CgA029qllFSWIBTqoRYwSLqWxFfZOdwFZ5X8ezj+WTPWLHgZ\ngfbOuIU5NSyFFt2ms66OLly37ltYu/i1rAnzfCUSAW7YeAsGjB4YZ+7zZqBIg1bkXEBFN9dc5PD6\ng9RI2nTIbNkKg3GVu2BJ8eYPrzsIzzE3Rl6m7NVgJOnY29Ih1Mlj8cvXYum2u3Dz1jsxYWktdb9u\ndxfeXLQaG+55L6WKROOuHodRV4zRtG/VJdWxjisG6Mjhb/HhdEObbjvolym4V3Yc7UCoswc1Cu0x\nW81wVrpgspjgrHDB5syMHdrmLICjwhm9aEYukRYhP4/dz/wTAHD2yzP49P5NeGXW81h/y9vyB5kA\nmIWCG5l4bnLue5PunIzau6bAas89XTVWL9ZAct79UEpRgRXbHtuaUvGHoDeIvS/sgr8tAIvVjJBM\npRatiJnt0t0nExSX2XHut6cgEhaEQ/W8mrgsfrSERbTkXmpc++K1qFo0Gkc+OKh6vP9UAL4WT8y9\nUsws6D/p15zkSAmr3SbvF6xGJIIJt52LRkoYPSC46F31xg2o+8E0fOPHF6DzdEDWnTQdau+cjEXP\nXYMJN9eipyuEtt2nDL9GuriPdWD/6gb888m/49QXrerulw4rwsEwrHab4JJI6SeDJg6BtciKoK8b\nDhWXWke5E6FOPubeO/8Pi8H7g0mZKC/+j0sw8vLRqL37PARa/eh2dyHoC+bEGpKzwoVv/PgC3UVi\n8t79UMTi4fGbsb/JyosYPGkIhl9UiX2v7KUuTIkhuRu+8158gikJcnmrs0K0w0rXCHqCPcp5nXV0\ncldVCX60/4fo8HfpClQSFzFFH/Mz5DTeXPhK2u/U6rAqehMp4aoqwYJnF8tG4QHAuBvG49L/XYie\nYA+aPz2G92//S6pNTYIW4KPHZTWfKBpUBKvdBl+zN2YWcVb1psqoHFuG39b9Hn5aUZBKF2788Jak\n/PeAfPBZ4vb37/qL7PeqhNYEdha7FXW3n4cvXtwlu6A9pLYMN310m+429JvFTle5C87KzNq4i8vs\nsaKwn63cgsIBhQj5eWq1855gj6w2aXMW4Ko1NwjV1jWGhRcNKULX6dTc0JKICsZE9z+lTIB6hGks\nC1ybF0c2HNZ83L7Ve3F4vVCgQBqar4fE3CNKBbUBwFJsQeWsETi2kR5OLth4TYof64E1+3Fmfzu6\n3N26+5/SIGO127D0b3fBMdQZ+y1TxYFFLA4relIc9NKl60wXBp0jrJVIq3c1rNoFs9WMMU9fieIB\nRVRBPuqKMSgebI8lC5OiVKhb3G4f6kC3zsBC8XvXmsDOZAL4Th43fngLXp/3Ino6k9d4ujq6VCtU\n6SWvBLnW5EepYh/mwKLnr8Ygbgg+W7kl7jq0aufeJo/sBxfq5BF0d2PmyrmYet90vDb3RcVFQWel\nC5f/6UpDtFMah9cdVE2epISj3InAKX+Sx4JeoSP1Z1fzLJGjpyuEq9bfDGuRNbaAKldQ22q34Zbt\nd6HAVSiv5YaFxV61lLKpzq6UTD493aGk7UWDi3UVvtaEWShFN/ziSnX33Qxz5kA79fcj7x/C+h+t\npz7nQRMG6/K9pmnovuNe6gCRiLPShZELRsUl05MmsJNmfXQfdcfFZ4T8Iex6fhf2vrGXKsQBwTde\nLTeQXvJKkAMJdfwM1sx5XxBvLnpFMUBDrHYOAKGuEJzlLmr5K2mucvtQB8ZcPU5xABq5YBSsRTYU\nDSlGVxs9k50Ui8OKSLd2VzZfixef3r8Jc59YoDv/uauqBDdsXEqd0hpRkTwRR4UTRaVFgtcPZVCz\nFltjpcFE5O5p4q21MW1XSQkQ614WlBYg6E7fZh+HwsBMyzz490e3ygvxVGy8ZmDsteMxY8VsdJ3p\nQvOnX8F/vA89dGS6rLfFg90v0tcpzuxvR/2Dm4WEbAo+2LQc6qJpcdfvdmhqXvn0CsxcGX+dxAR2\nAGA/x4435r9MDbRTGrxp7zxd8spGLq3nJ41yPPbXI/C2eNKOzNKEWai6IuYwNpnpochFg4pgdRTE\nOlPN5aMQCUew/7XGONuZ1WFF6cgB6PYEezVbHWu5o785Dsf/1ozOVm2LlZPvrcOMFXOEakYa85/L\n1c8U30c6KRSSMAPf2nw7SqpLZYNMbM4C3NnwXQBIqk8ql3ER0Fh7Ui1K02CkQTVipr2Xpv2JLsjT\nXKgT65XK1W7ta+zDHKqurGq1XOX64uCJQ3CGtGuWD7RUGYmDRKppAtTuQQ4lG3neCnIp6S5k6EGu\nILIWxIU+i5dH23EPrEVW7H1OvQq3Enoz6KkVn1YThlLE90E7rvyiChx4fV9K9yPmsX75wlV0wWUC\nuJsmUgsCiAu6AJK0dhG1rHSZJNFEdeHyWfhs5ZaYcCguc6DTYNc0OVR9rU1CebJs+dePvWE8vlyj\n7EKqVMDYyEVik8WESbdPjpsBpKqw0MySX/vIThqpLmSkRuoDn5jneGjtUJiH2sEHeNl8zlrxHffi\n3HvqEPQHNQlOaYSlUv5ztZzQUmjHAcDxv7Xo/qjERVT7UIfswrbNQS8IEAlHYDKbqNNq6YdjP8eu\nOZe49B7TDTCheV0kCodsCXFAMIvN++0ifPjddVRN2FWpT0mwFFswcPQgdJ3tgu+EF45hTljtNrgP\nnpU/KGq7F5OIHdtwWNEdVa6aEWDsInGkJ4KGP++C2WaOzZhSqYugZJY0khwMO0iNQKsffjlvjBSx\nOQtigSCuqhJwSyamFeKfGAhgRMdzDnfBWeHCnMcug7PSpWl/NfucKOD1djrpcWpBP4mYLCbU3jUl\ntgaifDxdAO9/tRG7n9kpCP9wr4DftuKTuP2CnqBuE9ykOyej9u4pQmxAilRcXIXiwfbYMzKqaEqq\n+E/64BzmxJirx1G31ywajZkPz9V031a7DUu33YXhMyoFExCEFM1Vs0cItQAo2Ic5cP37N2PJljsw\nc+VcFJYU4rw7z1O8jlL/FddrjOTI+4diM1elb1Uu8Khm0ei4d54p+o0gV3qJjnIn7MP0Ly5MWDoJ\nN9ffiaXb7sKSLXdg9qPz0uooiZ3QiI4narA2u01TlKWW4tNGMWPFHKFquLNAdd9IJIIp35sapzmL\nx7uqSjQNpnImL/FjFLEPdcBZRX/uzkoXau+aEnfNyffW4eL/vASzH52Hgdxg1XuhYgIsxdY4rV7v\nQG50ZKTYH2nPWVxLMVvNmPLdqVAzwU68tRa7frdDGEibvcJA2uxFw6pdKBpYRD0mcNKPDfesw/ZH\n6mPP5fL/ezkm31sHq8y9KvXfVJSHQROVCzuIypfSt+qqKsEt2+8G962JcYqf+AyzQb8wrQDKromV\nc0Yo+hknIuZ4FjuydBqn5PEhJhQqLC2kulAldkKlNovnE92cujq64oIopP7sInEePS0e2OwFQEIO\n7Gx1LKDX5DJt2QzUP7gZzVvkvSVcFSVJmpacyUbO1VCOxOm40nMfdcWYuMXHxDTCKZvvIkDjc7th\nLbDEFrqUPH5szgIUlBQIWnO5kPpUbjF6SG0ZQj4e7ia3UDM1mi1TLH5sLbZRBzlpf1QyqSm1U7Ql\ni/n+aXSe7cSgiUNwlrLYmBjnkNhnWrY2wX/Cp7n/itv3rd6rupY16Y7JmLlyLuqXb5bNUCoOdkp9\npmbRaDj+f3vnGmJVFcXx39zm5fjCMbUEmQprIYIG5SMf44CJlpQV9EUqU3oIQkWBlmmfjCyyQCMq\na7LMvmQZKZmGPUwteoOiLZmyPhhFiZqPfIxjH/a50/F677nnPubeu8f1g4F7z5577vrvve86+6y9\nz9qDejF5xTROHz9NfTucqKZkAyboRo4cznVk4cm6UfPHxf/xJ2D6mlsz5nCI+o5k/vCoFRT5nC88\nMVnbpzZjvC2T48sl3t0V1PWp6+zkWxdsSbuOOdtIK+7FNG1ysDS34+Ec90kHGG6jdA+YZAvf9by0\nF1dMH0pHewe7V+9M6xhS92jNpGXYzOGRm3Gn9qvGvg38tuuPc9p57KKJHP/zGPX9e/DNMzuy9sdM\nD9VE2Tl81gial07m8L5DGe8uju0/mnWzi/A+mXBun8l1vmbMwgn88mFbRkeeOlBrXjoZzpJ2LiDc\nLzP9VsP1WNNQQ2OGRRldSbdYtZJKusaPO+McNSue7Tvi/k86Hbl22EogbnuEyXVlTC7nyHX5JNC5\nUW6ceo9aFZHcyaZH/4bIXZCSe1kmHWY+9ZGur8Rpi0L6WDY7o+omzsRysl6Gjh5SsBOM3IUqWN6a\nOlDLpR2y1WM+v4s4dPvlh3FIbahMt5v5rvHMha5q6FJTiI5iXLgKXT6Zj4ZMA4Jwv4lyalHb0RVS\nH6XqU1F2FvI8QbJeBjc1Fqwjn/oPf7bQflkOR96tQitRpIYd4t5uGl1DttwY+Zwj3+WTuRD39joq\nnprOpmLURymIsjNd3TRNuZxfP96XNaxZzEn4fOo//Fkf2iGVC2ZEnolyhDRsRF455KshW78pRggp\nFyqpLVLrJmqkHp60T1Qniqaj1PUfpiJDKyKSAF4ERgIngXtUtS1UfhPwBNAOtKrqyqjzVZojLwem\no3Loag2lGihUclukc6pNQdraZFKqJMXW0Z0GaoWGVm4B6lX1OhEZCywDZgCISA3wPDAKOAZsF5EP\nVLX4WfcNw0N8vVUvJqUIeWXiQqn/OPcYE4CPAFT1K+DaUNkwoE1VD6rqKWAb0Fx0Kw3D8J58nxg2\nshNnRN4HOBx6f0ZEqlW1PU3ZEaBv1Mn69Wugujq3LY7CDBiQ/TF0HzAdlUN30ACmo5IotYY4jvwf\nIGxVInDi6cp6A4eiTnbwYO57Qyap5DhgLpiOyqE7aADTUUl0YYw8Y1mc0Mp24EaAIEa+M1S2B7hS\nRBpFpBYXVvkyf1MNwzCMXIkzIl8HTBGRHbi8ZrNFZCbQS1VfEZGHgU24i0Krqu7vOnMNwzCMVLI6\nclXtAOamHP4pVL4eWF9kuwzDMIyYlPyBIMMwDKO4dJt85IZhGBcq5sgNwzA8xxy5YRiG55gjNwzD\n8Bxz5IZhGJ5jjtwwDMNzzJEbhmF4jhc7BGXLiV6piMgY4GlVbRGRocAq4CywC5inqh0ici9wPy6f\n+xJV3VA2g1MI0hS3ApcBdcASYDce6RCRi4CVgOBsngucwCMNYURkIPAdMAVn5yo80yEi3+PyNAHs\nA57EMx0i8hhwM1CL802fU0YNvozIO3OiA4/icqJXNCIyH3gVqA8OPQcsUtWJuFQHM0TkEuABYDww\nFXhKROrKYW8G7gAOBDZPA17APx03AajqeGARzmn4pgHovLC+DPwbHPJOh4jUA1Wq2hL8zcYzHSLS\nAozD2TYJGEKZNfjiyKNyolcqPwO3hd5fg7tqA2wErgdGA9tV9aSqHgbagBEltTKad4DFwesq3KjC\nKx2q+j5wX/C2CZed0ysNIZ4FXgJ+D977qGMk0CAim0XkkyARn286puKSB67DpSfZQJk1+OLI0+ZE\nL5cxcVDVd4HToUNVqprMh5DM255zPvdSoqpHVfWIiPQG1uJGtD7qaBeRN4AVwBo81CAidwN/qeqm\n0GHvdADHcRekqbgwl4/tcTFuMHk7/2tIlFODL448Kie6L3SEXifztuecz73UiMgQ4FNgtaq+jac6\nVHUWcBUuXt4jVOSLhjm4LKSfAVcDbwIDQ+W+6NgLvKWqZ1V1L3AAGBQq90HHAWCTqp5SVcXNuYQd\ndMk1+OLIo3Ki+8IPQWwN4AbgC+BrYKKI1ItIX9zWebvKZN95iMggYDOwQFVbg8Ne6RCRO4OJKXCj\nwQ7gW580AKhqs6pOUtUW4EfgLmCjbzpwF6RlACIyGDdq3eyZjm3ANBGpCjT0BLaUU0NFhydCnJcT\nvcz25MMjwMpgA449wFpVPSMiy3GNngAeV9UT5TQyhYVAP2CxiCRj5Q8Cyz3S8R7wuohsBWqAh3B2\n+9YW6fCxT70GrBKRbbgVHnOAv/FIh6puEJFmnKNOAPNwq2/KpsHS2BqGYXiOL6EVwzAMIwPmyA3D\nMDzHHLlhGIbnmCM3DMPwHHPkhmEYnmOO3DAMw3PMkRuGYXjOf746asag4Rh+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2c1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"DiabetesPedigreeFunction\"].values,color='purple')\n",
    "plt.title(\"DiabetesPedigreeFunction\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lnq/LPRKJcPh0Bz88cI7Tzb2khQJ84n2ree/WGv1KKTJPmekhlle6F69ZUZVP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\n8SGVu4iID/1/Aew0dEZhrtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f0a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#糖尿病家族史\n",
    "fig = plt.figure()\n",
    "sns.distplot(x_train.Age.values, bins=30, kde=True)\n",
    "plt.xlabel('Age', fontsize=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BsJt3tkPRMq3lRwsrApDyb9LGDQDuKg9GhkcQ9cvlXy5K62cm45IrRViuQ67k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3a2910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(x_train.shape[0]), x_train[\"Age\"].values,color='purple')\n",
    "plt.title(\"Age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=x_train.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "x_train_corr = x_train.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gfX3p1qljof19+/1aWhSD6UVXNqjPNytWAbBx8xaqV63qa6tSOYHUvXs5fjx/XNZvoEG9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\nYowxxhhjjImo/wdY8dyr5W8r7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8c2090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(x_train_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(x_train_corr, mask=x_train_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('Outcome.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies and Age = 0.54\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = x_train_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (x_train_corr.iloc[i,j] >= threshold and x_train_corr.iloc[i,j] < 1) or (x_train_corr.iloc[i,j] < 0 and x_train_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([x_train_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以发现只有怀孕次数与年龄相关系数比较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vaqJrTQsNRUPf7t+1kTs2r4m9bmfJuu9KYN1oKOKVq1cZGZtkc8/qxIYiaphc\ngfUgx0iWY9YwuQCqmtKqjLGS5Yob63XBbshgdL2Vrhv3erPqO6ZhcuWFUmlaLImYVe2WIFUf2Qtx\nmJzVkMHoeitdN+71VqnvWBwaJldeFs94bsQ6ZiWfBVD1kb0Qh8mFNvTNKl4Nk5OFUPJZgOLqo+LD\ncFD1UVLSGCZX+tzF3ePQhr4Vx1u6F3HiLd7j4kIG0DA5JcfZcjlSAYpaRAy+wpae5FqH7HCd1NUs\n47XXpxgZm6RvUzvNy+u4fXNHrHUtB2WFxnKY3OSFqVnPXRJ7HNrQt7f2dlFbw6y9eMub4u/xqpZG\nfjg8zqnh1+jb1M76zla2JtCqxvI1YvV+Yd3CKGS5TD6lB5MnTp9jz8CpRA4m21sbefrwzJvM4NA4\njfW1vL1/Xey4d/Z2XXsz6GhbQfPyXD4982K1F1bP3YULl3n/3bdycniCk8MTrOtsYV1nCxcuxBtX\n0L+pnYc+0Jd4f7DWFfUme3Fu8hL/48mXrlt3oH6EdQ9si7UuFGb5PPzgjsT7FFq9X8xa90xy70N5\nkMt3N8sBalZr7z18hicPvkxjfS1tKxs5dHyUC5cus6KxfsldqFZ7YXldPPPiCN999jStTfVsXLuS\n546f5bvPneaubWvZ8cZ4ZdHR0Lckq5qs9sJ6gJrFMDnL17TlIMfQ5e7MJ8RDz+J1i6ua4q4bIqu9\nsLwuVqyov1YYMH7+Es8dH2X8/CWgUBiwYkX9ote2YLUXaQ5QSyrxhFjgkhe5Sz6WB5NpHIYnuW6I\nQtzjyclLFQsDJicvLXptC1Z7EeIAtRCvt7zIXfIB2xYRVmun0dYilNG91nts0bbn9ts6ysZ8+23x\nClGsWO1xiIMcQ35Nh8y0w4FzrhM4ALwDmAK+CFwFDgG7vfcVfxSK0+HAcoCa1eApq5gPnRjj6cND\n/HBonPVdrezsNRisl3CVUIjDvfa9cJZnXxy5tva22zq4M+Z5T7Gkf5M9xD22qh6zeu2FOMgxSVUZ\nJuecqwf+AugF3gf8FvC73vs9zrnPAY97779WaY0stteJKljg+nYnWW3xYdVGBezb4ECye5zGXty0\nsoG3bF3L946c5kfnLmb2urDaY8vXh9rrlBdq8rG8D/PbwOeAl6f/vgN4YvrPjwH3GT62maiCpbTd\nSVZbfFi1UQH7NjhJ73Eae3F6dJJHn3qJ06OTmb4urPbY8vWh9jr5YlJq7Zz7MDDivX/cOffw9JeX\nee+jZ2COGDVJAAAgAElEQVQcuGGL4ra2JurqFtdCpFhHR/lD0MV4fnDuCpYkHyeptSpVH8V9DKu9\nsFo3xL0oldRaoT131msXS3KttIQYs9Xv+XwUuOqcuw/YDnwZKP6V7Fag/JVUZGzsfOxAkv5IuqVn\nNSdOn5v1ddfTltjjJBnz+q5WBodmr9XTHf8xrPbCat0Q96JYktdFaM+d9dqRLN/CmkuWY66UFE1u\nu3nv7/be3+O9vxc4CPws8Jhz7t7pf3I/8JTFY1sLrYLFsvootCqhNPYilOFsIVYUhvbaK6bf65nN\nfJ6Pc24P8DHgCvAI0AAcBR7y3lfsPZLFggOwr2CxmEFkW30UTpWQ5V5Yrb3/2FkOHpupotu+uSP2\nMDkIuboynNdeGr3dMv7JJ/1qtyRkNflErCpYQqhqKhVClVC0Fw31NWxcu5IfnD7HxUtXEh0ml3Ql\n3f5jZ/n8o4dnrftz7+tNJAGBzR5bD0UM6bVnvRehJp8wfuswo0KoYAmtOq9Uknsc7UVxC5ykh8kV\nS6KS7uCxkbLrHjw2EmvdYhZ7XMziegvptVcspNeeNSWfHFN/qRkhDpNbvryu4jC55Rnreq7rbYb2\n4saUfHJM/aVmWO6FVU+z11+fqtgz7vXXpxa1rhVdbzO0Fzem5BNDCL3SLKuP0rDYUdHlWFZLWVXS\nbd9cvmfc9pjDC4uF0CctRNqLynJbcGDVcwxsq3nApodXaL3drCrHQuz5Z1vtlnwlllW8lkLrUQi2\n73FJWXLVbpZVJpb9wSKhVNxYrT1wfJRHvn5o1roPfaAvsbJoywqhuroak/EBIVwX0XMH11dXJvnc\nJS20HoWWMSdtyVW7WVaZWPYHs2C5F1ZrD/jhsusO+OFY66Yli3NrSlk/d6XVlVl+7kLrUVi8drHQ\nKulyl3xCrGqyEuJU14aG2ooVXkmeAS1Veu5mhDjJNC+VdNl6t0xAiFVNVtKa6lpczBB37YsXL1+r\n8CpuVQOFCq+LFys2xciELL7JFrO6Loqfu1JZfe5CnGSal0q6bP2iQEJ29XazZ+DUrPuhSVU17T86\nNGvtpCc1JsVyL3b1djN5YYrXXp9iZGySvk3tNC+vi732Ha6TdR0t/HB4nFPDr9G3qZ31na1039QU\nO2bLQ1rbIolkY7a6LvpdJweODs9at991VvivqstqL6xfe1ZrpyWXBQdg23Ps2wMnr73hdrStoHl5\nHW/vX5fZgVZWe7H/hbN8/htlWr+8t5c7YkzwPHDsLH9UpqXMP31fLztiVE1ZHtJaFUlYF4yE1j/P\nSmg9Cq3XTkqlgoNcfvIB6OlsoaezJfE38r2Hz/DkwZdprK+lbWUjh46PcuHSZVY01mfuiY9Ee5F0\nP6yDL87R+uXFkVjJ55k5Wso8c2wkVvKpdEgbO/lUKJKI88ZrGbPVddG/qZ3+Te2Z7jlWyur9wmqP\ni9cOaZ+L5e7Mx1LxQV9xBQuEcdCX5MW/YkV9xcPlFSvqF7WuVUsZy0Naq4P2tA6WQzkjCJn2eDYl\nnwXIy0FfEiYnL1U8XJ6cvLSoda1aylg+d1YH7breJM+UfBYo5JYZSZeC335b+dYvt98Wr/WLVUsZ\ny+eu33WWbWMU96A9jest65/YJZ9qP/WpT1U7hjmdP3/xU3HXaG5u5Pz5iwlEU7CquYGNt6yitrbw\nRv5jt63hg/dsYvMtqxJ7jKRjPnRijG989wRff/Iljp8ep6Ghls7VK2Kve0t7E51rWmior2EZy+jd\n1M67dm3kzhjnPQA3tzfRVbLu/bs28paY61o+d2tvKsRcU7uM1yan2LKxjft3bYzdVsYy5sHhCb65\nb5Cv7nmJobHzrGxuYFVzQ2Lrfvmx5xNdNw1Jv/bSkOWYm5sbf3Ou7+W22i1iUTlmPZwtyZgt2wFF\na7c01dF36xoOvXSWifNTsdeO9vimlQ28ZetavnfkND86dzHT7U6s9tkqZuuWMllv+zKXEA/vsxzz\nkmuvYym04WyW7YCitUdfvcATz5xi9NULiawd7fHp0UkefeolTo9OZr7didU+W8Vs3VIm6XUlf5R8\nFiC0thaW7YCs1g6x3Yn2wnZdySclnwUIrfrIsh2Q1dohtjvRXtiuK/mk5LNAoVW7WQ05s1zbao8t\nB+uFuhehrFsqa018ZeFyW3BgP0DNrq1F0geIlu1OrNa2GkhmeV1YDQ4LrfWL9cC+kAY5piHLMS+5\nYXJpDHwDTFpmgM3QsIb6GjauXckPTp/j4qUriQ60gmQrsawGkqVViRXCMLliIVzHEN4gx7RkOeYl\nV+2W1sC3EO5hR9VH4+cv8dzxUcbPX0p8oFXSlVhWA8nSqsTK2miNGwnhOobwBjlKZblLPqENfLMU\n4kCr0PukiQ29rvMnd89YaAPfLFkNfLNcOw990kJLZCHEq9d1/uRypEJoA98sWQ18s1x7xxwDyXYk\n0CfNcgDXtaFvJ15hy4bkBtVZDcCziteqKECv63zJZfLp29DG7ge2mVQehebc5CWePjzzgh0cGqex\nvpY73pTMC7bc2m/vXxdrzfbWRnb2ds0a2Nfe2hg73nLrJqG0mOHEmXPsGTiVeLuaE6eN1k0o3tKi\ngMGhcfYfHUqkKECv63zJZfKBwoXat6Et05Ugaah0SBv3RWs16MxqYJ/lIEDLvQhpXcvrDWZe11YV\nhZKe3J35yAzLQ9o0WrQkObDPchBgaO1qrNZNsyhAiSd8Sj45ZnlIG1qLFsuCg9BitlpXRQGyEEo+\nMYRQ3mnZXseqXU1o6xavXSzL7Wqs1rW83iRfcjtMznKgldVwNouYO1evKDuMLIn776uaG+juaKGm\nZmaA2rvv2sibesr/VL2QdTfesoqrV+G1yUu8aeNNfPDe+APUrNaN1t522xoaG+q4fOUqO3u7+an7\nNsc+S7Jct7ujhfqigX1JPHeW11sasjyYbS5ZjnnJDZOzbKNiPTTMsvVL0oe0URuc0piz2gYnrfY6\nIbSrsWphVCzEYh/FnKwl117Hso2K9dCw0nWTbP2S9D33qA1OsSy3wUmrvU4I7WqsWhiJzFfuko+G\nhqUjtDY4Ie6xFavnTmQhcpd8NDQsHaG1wQlxj61YPXciC5G75AMzlTytTfX82KZ2Wpvql+zQsGJJ\n/3Tf7zrLxtyfQBuc0KrdioVQBWn13InMVy6r3VY1N3BzVytXrl5lZGySzT2rec/b3sCW9fEqeaBQ\nzVOuSmj7rfEOaa2qmmCmiu6re15KtPJv7U1NZfdix23xhr5Fz9+yZVyrokvi+bNaNxJSFaTVc1cs\ny1VYc1HMyVpy1W6WQ6eiteH6KqGsDrSyrPCK1m5pqqPv1jUceuksE+enEu8PFsUcd4/TuC5CqYK0\nHDIYyXIV1lwUc7KWXLWb5dCpaO3SKqGsDrSyrPCK1h599QJPPHOK0VcvJLK21fOXxnWR9NrWlX8W\nQwZF5iN3yceyv1RoA61CHCZntcchXheq/JM8y9a7ZQIs+0uF1rsqxH5mVnsc4nWhyj/Js9wlH5ip\nSCutakqiv5Tl2hYsq+iitTd0NfMz79zChq7mTPcHs+w7ZnVdhNbbTWS+clnt1rl6BV1rWqipnek5\ndv+ujbw5gbYhUbVbaT+zuNVuMFMt9aff8olVS0UVXnV1M1VNSVV4rWpuoHNNC5MXL3Po+Cjrulp5\n166NbM1of7DO1SvK7sXtb7gp1rrR2hbXXNSPrnQvkuhzZ11dadFX0VqWK8fmkuWYl1y1m1XPMQiv\nEsuyOu97L5zlC984PCvmj763l7e8MZmS3ST70VlWeFn3uYPrn78kq9KS7EWXVv88K1muHJtLlmNe\nctVuVj3HILxKLMvqvO+/OFI25u+/OBJ77UiS52iWFV7Wfe5Kn78kq9KSPONJq3+ehC93yceyb1Vo\nlViWFV4rVtRX3OcVK+oXvbYFywqv0PrcWQktXqmu3CUfy75VoVViWVZ4TU5eqrjPk5OXFr12seXL\n6xJZx7LCK7Q+d1aK4y0uvIBsxivVlcwruwznXC3wCOCAq8DHgNeBL07//RCw23ufeH1yv+vkwNHh\nWfedk+hbtbO3i/1Hh2atnUQlVkjrAmy/raPsPm+/rSP22vuPneXgsRFODk2wrquF7Zs7uGNzvHOk\nXb3d7Bk4NSveJCq8rK45y5gt7OrtZvLCFK+9PsXI2CR9m9ppXl6X2XilesySD/BeAO/933PO3Qt8\nGlgGfNJ7v8c59zng/cDXkn7g/k3tPPSBPgb88LU3r37XmciQrGUU3tCjF1dH2wqal9cR94ZC34Y2\ndj+wjX1Hhhg8M05Pdyt3bu2KXRRw8eJl3n/3rZwcnuDk8ATrOltY15lM5+La2mVl96K2Nt5u7D92\nls8/OlPIMDg0zoGjw/C+3lgJqKezhYcf3MHew0P4wTFcTxu7ersSOQi3uuYsY7by9OGh6567xvpa\n3t6/rspRSdaYJR/v/dedc381/dcNwCvAfcAT0197DPgJDJIPFN4M+je1J14J8vSRIf7m+y/TWF9L\n28pGDh0f5cKly1y5Cr0xE0Xfhjb6NrQlO7Hy2AjfffY0rU31bFy7kueOn+W7z53mrm1r6Y/5SeKA\nH+a7z56etRdTV67GKjE+eKx8IcPBYyOxP/30dLbQ09liUiEUXXMNDbWJjiWwjDlplQoOspwwJX2W\nn3zw3k85574EfBB4AHiH9z668TsOVPxlhba2Jurq4g+26ugof+6xGNEBflR9VPz1JB8nqbWig/Co\nwqv463EfI1q7dC/irl3p8D6Le5ymrMf8/ODcBQdZjz0SSpzFQozZNPkAeO//iXPuV4GngeLfmGyl\n8GloTmNj5yt9e16S/mlxfVcrg0Oz1+vpTu5xkox5XVdL2XjXdbXEfgyrtS1jjoTwKaJUCDFv6VnN\nidPnZn3d9bRlPnYIY49LZTnmSknRrNrNOfegc+7h6b+eB64A+6fPfwDuB56yenwrURuV0kF1WW2v\ns31zR9l4t2+OXxRgNZAsirm0VU0SMctsSZZApzWwT8Jn1uHAOdcM/DHQDdQD/x44SqECrmH6zw95\n7+e8Ob7YDgdQ+M3+pw8P8cOhcdZ3tbKzN/7hfcSiEgvsYt73wlmefXEm3m23dXBnQh0IrNa2Wtfy\nurCW9E+4g8MT7D18hudPvMKWDavZ1dudyLmM9jhdWY65UoeDXLbXCXFoWGjrwkx7Hbi+9Uvc9joh\n7kUaQhgyqPY66ctyzEuuvU6IQ8NCWxdm2uuUtn6J214nxL0IjfWQuqTXlfzJXfIJcWhYaOuCXXud\nEPciNBpSJ1mQu1dciEPDQlsX7NrrhLgXodGQOsmC3CUfCHOYXLRu+6pG7nnzLbSvasz0ugC339ZR\nttrt9pjtdUIcJhcaDamTasvtMLmbu1pZtoxrg72SHBrWuaaF2qKhYe9KYGhYtO7lK1cZPDPOrbes\nyvS6ALe0N9G5poWG+pnhbO/atTF2VVo0mK143SQGs0V7URpvEnuRhiSHhlkNk7McUpeGLA9mm0uW\nY15yw+QsK272vXCWPy4zQO0j7+2N9aZrNZjNcuBb1IMNrq92+7mYPdiiwWyl68YdzGZVnZcWq6qm\nJIfJFctyFdZcFHOylly1m2XFzbNzDFB7NmaFl9VgNsuBb1EPttJqt4PH4q0dDWYrXTfuYDar6rzQ\n6SxGqiF3ycey4qapqaFihVdT0+Lm1FtVjlkOfFu+vK7i2oudw2M1mC204XcieZe75GNZcXP+/MWK\nFV6Lve9aXDlW3AYnWnexlWNW6wK8/vpUxb14/fWpRa1bPJitdCBZnMFsaQ2/E5H5MW8sWg2WA7hu\nn2OAWtwKr/7bOljX0cIPh8c5NfwafZvaWd/ZSueqFTf+j6uwLhR6sJUdJhezB9sdrpO6mmWzBpLF\nXdfquRORhctl8hkdv1B2yNno+IXYBQf1tcvKDmerjzlA7TLwP5586bohXAP1I3z0vb2ZXBegqaGW\nn/oJxws/HLvWg+2N69toWuStschVyg8kuz1m8ulavbzsc9e1enmsdUVk4XKZfKyGnAHsn1673HC2\n7THWrlQYEKcSy2pdgL+dHqzXvqqRvlvXcOils3z32dO87fab2RqjV9oBP1w25gN+ONbzt/fwGR5/\nenDWc/fOnRuCKQUWyYvcnfkUH1gXVzVBvAPr0rWj4Wzj5y/FXrulpXIhQ0vL4goZrAok4Pp2NaOv\nXuCJZ04x+mqhdDlOuxqrgoPiQpTS506tX0TSl7vkU3xgXSrOgbXl2hMTlQsZJiYWV8hgVSABdu1q\nrPZYrV9EsiV3yQdmhpxt6GrmZ965hQ1dzYkMOSteu3Q4W9y1t83RqmZbzMNwq3XBrnWP1ZA6DToT\nyY5cttdZe1Oh7cvkxcscOj7Kuq5W3rVrYyID36K1AUbGJtncszqRta1a1VitC3ate9be1MTNXa3U\n1c3E/J63vSFWdwMotH4p13Zpy/ryn4iyJsttVMoJLV5QzElbcu11rFrgWK4dtX5pqK9h49qV/OD0\nOS5eupJYe52WprprRQET56cSba9Tuhdx2+sUa2iojXWrtJiGyaUrtHhBMSdtybXXsWqBY7l2VJVW\nfBieZHud4qKApNvrFEuivU6xpBIPaJicSJbkLvk0N1eu8GpuXnyFl9Xaaq9jT8PkRLIld6+4116r\nXOH12muLvzdqtbZV6xfLljJW7XWsaJicSLbkLvnATIVXaVVTEhVeVtVj0WC20sqxuK1fonXXtq/g\nfT9+K2vbVyTWUmb75vL7HLcNTrE4v5dVSsPkRLIjlwUHUCgMePbFkWttX7bd1pFIhZfl2qGta7n2\nwPFRBvzwtXX7XWfsajcoFB3sOzLE4JlxerpbuXNrVxDFBpDtg+VyQosXFHPSKhUc5DL5WFa7WQ0k\ns4rZci+sqseiYXKl68YdJlcsyy/YuYQWc2jxgmJOmqrdSK7azWogmVXMlnthVT02MEdvt7jD5EQk\nO3KXfFpbK1ektbYuvtrNqnps5crKMa9cubiYrXrGgV31mFVvNxHJltwln/HxyhVp4+OLr3Yrrh4r\nN+hssdVj585VjvncucXFbNUzDsLr7ZYmNSkVubHcJR+YqfAqllSF147bOrh7+830bWqnoa6Wvk3t\n3L39ZnZktAdbGr3dStfOam83a4PDE/z5d17kN77wPf78Oy8yOFz+E5yI5HSeTw2UHSaXRKa9RPlB\nZ1tvjXd4Xw9lB7Mt/tdACxqXwYPvfhNH/m702rpb39BOYwI/nE9NXSm7z3F/Z2ZNa2PZvVjT2hg/\naCODwxN85isHrl0XJ86cY8/AKR5+cIdmBYmUkcvk88yLI3MOk9sRs8Kr0gF+nOqxA9Mxlw5mu2vb\nWt4cp7fbscK6a9tX8Jata/nekdPX1r09Zv+1/RWG9sUZrBcNfSvdiywPfdt7+EzZ62Lv4aHMxixS\nTblLPsUtcKKKtEjUAmexnQjmUxiwmPOZ4qFvUQ+24nWbmhoW1bW2uAXO6dFJHn3qpevWXb68btGd\nCMoN7Stee7ENQYuHvpXuRTT0LWuzd4pjLpXVmEWqLXdnPpbtdawKA6yGvlm2wNHQtxkhxixSbblL\nPjBzyF468C3J9jpJt+6xWteyBU5UGFC6dlJD34plfehbiDGLVFMuOxxAmC1lQlvXcu3B4Qn2Hh7C\nD47hetrY1duVyNlJYd0zPD/4Clt6VrOrtzuxMxmrmCNZ/k32ckKLFxRz0tReh+wPk7Na13KAWhpt\ncJI8LymtSINCvElXpFmd8WT5Taac0OIFxZw0tdch+8PkrNa1HKCWRhucJN/EK1WkJUlnPCI3lrvk\nU1w5ViqqHFssq3Y1Vi2BLAeohdYGZz4VaSKSntwlH6vKMbBrV2PVEshygFpobXBUkSaSLblLPpBO\ntVuxLFe7RS1wStdNYoBaaG1wVJEmkh25LDiAMCu8QlsXYP+xsxw8NrP29s0d3BGzc4Il64o0C5YV\nepayfBA+F8WcLFW7kXy1G1w/TC6r1W6WexFVj8H1exFCP7Msv2CLpVWhZyGUPS6mmJOlajeSr3Yr\nHSaX1Wo3y72IqsdK9yLp6rGlLK0KPZG05S75WA1ms1zbal3LwXqqHrOnPZY8y13yseq/Zrm21bqW\ng/VUPWZPeyx5lrvkA3aVY8VrF8vy0DfLYXKqHrOnPZa8ymXBAajaLY11odBiZ8APX1u733Um1lrH\nUpYPaUuFWKEHYe1xRDEnS9VuhFPtlvS6Uf+10nWT6L8W9Y0rXTuJvnHWsvyCnUtoMYcWLyjmpKna\njXCq3ZJeN+q/VrpuEv3Xor5xpWsn0TdORPItd8lH1W4zLPuvWfaNE5H8y907hKrdZlj2X7PsGyci\n+Vdnsahzrh74ArARaAT+LXAE+CJwFTgE7Pbem7xDbbutgwNHh2ed+SRV7WaxttW6/a6z7LpJ9F/b\n2dvF/qNDs9ZOom+ciOSbSfIBPgSMeu8fdM7dBByc/r9Peu/3OOc+B7wf+JrFg9/5xjXw3l6TCi+r\nta3W7d/UzkMf6DOpSOvb0MbuB7ax78gQg2fG6elu5c6tXZkvNhCR6rNKPn8JfHX6z8uAKWAH8MT0\n1x4DfgKj5AOFN/M737jGpBIkWnvlyoZYt/HmWjfpmPs3tdO/qZ2GhtrERx30bWijb0NbpituRCR7\nTEutnXOtwKPAI8Bve+9vnv7624GPeu8/VOm/n5q6fLWuLltDyUREZN7mLLW2+uSDc249hU82f+i9\n/xPn3G8VfbsVKN+0qsjY2PnYcYT4E7lithdavBBezKHFC4o5aR0d5YuSwKjazTnXBXwL+FXv/Rem\nv/yMc+7e6T/fDzxl8dgiIpJ9Vp98PgG0Ab/mnPu16a/9AvAHzrkG4CgzZ0IiIrLEmCQf7/0vUEg2\npe6xeDwREQlL7n7JVEREsk/JR0REUqfkIyIiqVPyERGR1Cn5iIhI6pR8REQkdUo+IiKSukyP0RYR\nkXzSJx8REUmdko+IiKROyUdERFKn5CMiIqlT8hERkdQp+YiISOrMJplWm3OuBvhD4HbgAvBPvfcv\nVjequTnn6oEvABuBRuDfeu8frWpQ8+Sc6wQOAO/w3j9f7XhuxDn3MPA+oIHCpN3PVzmkOU1fF1+i\ncF1cBh7K8h4753YC/8F7f69z7jbgi8BV4BCw23t/pZrxlSqJdzvwHyns8wXgZ733Q1UNsIzimIu+\n9tPAv/De76paYAuU508+HwCWTz8Z/xr4nSrHcyMfAka99z8OvAv4f6ocz7xMvzn+Z2Cy2rHMx/Q0\n3buAv0dhvtT6qgZ0Y+8G6rz3dwH/Bvh0leOZk3PuXwF/BCyf/tLvAp+cvqaXAe+vVmzllIn3/6bw\nBn4v8N+BX61SaHMqEzPOuTcDP0dhj4OR5+TzNuCbAN77vwXuqG44N/SXQDT1dRkwVcVYFuK3gc8B\nL1c7kHl6J/Ac8DXgG8BfVTecG3oBqJv+JL8SuFTleCo5DvyDor/vAJ6Y/vNjwH2pR1RZabz/2Ht/\ncPrPdcDr6Yd0Q9fF7JxrB/4d8ItVi2iR8px8VgKvFv39snMus7cZvfcT3vtx51wrhRHjn6x2TDfi\nnPswMOK9f7zasSzAGgo/iPxD4GPAf3XOZfknxgkKt9yeBx4B/qCq0VTgvf9vXJ8cl3nvoxYq48Cq\n9KOaW2m83vvTAM65u4CfB36vSqHNqThm51wt8Hnglyjsb1DynHzOAa1Ff6/x3mf604Rzbj3wHeAr\n3vs/qXY88/BR4B3OuT3AduDLzrnu6oZ0Q6PA4977i957T+Gn244qx1TJv6QQ7xspnF9+yTm3/Ab/\nTVYUn++0Aq9UK5D5cs79Iwqf5P937/1IteO5gR3AZuA/AX8GbHXO/X51Q5q/zH4SSMD/B7wX+Avn\n3Fsp3GrJLOdcF/At4Oe9939d7Xjmw3t/d/Tn6QT0Me/9mepFNC9/A/yCc+53gbVAM4WElFVjzPx0\n/iOgHqitXjgL8oxz7l7v/R7gfgo/WGWWc+5DwD8D7vXe/6ja8dyI934f0AvgnNsI/Jn3Ppjbb3lO\nPl+j8FP5dymcoXykyvHcyCeANuDXnHPR2c/93vsgDvJD4b3/K+fc3cA+Cp/8d3vvL1c5rEp+D/iC\nc+4pCtV5n/Dev1blmObrl4FHnHMNwFEKt5MzafoW1h8Ag8B/d84BPOG9/42qBpZj6motIiKpy/OZ\nj4iIZJSSj4iIpE7JR0REUqfkIyIiqVPyERGR1OW51Fpk3qZ/T+IF4AiFRpgNFFoGfcR7f7KKod2Q\nc+6g9357teMQWQglH5EZLxe/iTvnPkOhy/EHqxfSjSnxSIiUfETm9iTwPufcD4CnKbQQirqO/yKF\n29YHKPyi6uvOuZ+k0Hn6PDBAoRv1h6f/+69QaGraTKFV/wHn3D0UulQ3UfgF43/lvf9L59wXKfQl\n3AGsA37Te//HzrmbKPTy2kKh5f8vee+/7Zy76r1f5pxrAT4L9FHogvAfvPd/6pzbBvwXZpplfsR7\nf8xq00TmQ2c+ImVMj4r4RxTaNAE85r13FPrAPQTcNf2JYxj4FedcB/D7wP9GoXHpTSVLjnrv76TQ\nN+wT01/7FxTmTPVTaIn/60X/fj2FRPdeCp3DAf5P4EXv/ZuAB5k9XuGTwAHv/Q7gbuD/cM7dSqE/\n3O947++g8EnurYvYEpFE6ZOPyIybnXNRS/1GCi14/jXwExQ++QD8fQrNHP92ugVLA4VPOT8O7PXe\nnwJwzn2J62/XfXP6/x9ipiX+h4D3OOf+IYWE0FL077/lvb/qnDvETCK7B/hpAO/9c0Dp4LD7gCbn\n3Een/95MoffX/wQ+65x7F4UREpltcyNLh5KPyIyXy52fTCeZqMdeLfAX3vuPT3+vhcLr6B4q30mI\nZsNcZWbo11MUmm3uAf4a+JPSfz+dgKKvXTfLxzm3hUKRRKQW+JD3fmD6+13Aj7z3l5xze4H3ULhd\n+G4Kn95Eqka33UQWZg/wQedc5/QcoP9E4Q39u8BbnHNrp7/+jykkmrKmz2/eCPy69/7/pfDp6kbd\nqrml1CEAAADrSURBVJ+cXjdKPN8seYxvA/98+vtrgWeBHufcnwN3eu//M4WBhf0L+l8sYkDJR2QB\nvPffB36Twhv9YQqvoX8/Pfvl48D/Ar5HYfTBnB3Jp1v2/xFw2Dn3DNBJ4ZZZc4WH/w1gs3Pu+8B/\nBR4sGtbGdFwrpm/VfZtCAcNxCpMuP+GcG6BwfvRLC/9fLpIsdbUWScD0OOOPU6hMu+Kc+wPgmPf+\nP1Y5NJFM0pmPSDJ+BKwGDjnnpigUITxS3ZBEskuffEREJHU68xERkdQp+YiISOqUfEREJHVKPiIi\nkjolHxERSZ2Sj4iIpO7/B8ev6gUxr+DIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a09b890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(x_train, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic 回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_x = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "x_train = ss_x.fit_transform(x_train)\n",
    "#x_test = ss_x.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [ 0.46129644  0.46510588  0.56426612  0.44377717  0.46617809]\n",
      "cv logloss is: 0.480124740047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/py27/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, x_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print 'logloss of each fold is: ',-loss\n",
    "print'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "值较小，进行参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00059547,  0.00062299,  0.00058856,  0.0008266 ,  0.0006732 ,\n",
       "         0.00068998,  0.00074081,  0.00063958,  0.00072598,  0.00060458,\n",
       "         0.0006474 ,  0.00059938,  0.00061617,  0.00057659]),\n",
       " 'mean_score_time': array([ 0.00070095,  0.00060058,  0.00069542,  0.00073662,  0.00059037,\n",
       "         0.00056782,  0.0005384 ,  0.00048656,  0.00047083,  0.0004643 ,\n",
       "         0.00045977,  0.00045662,  0.00045567,  0.00044765]),\n",
       " 'mean_test_score': array([-0.69314718, -0.64214833, -0.6721329 , -0.52844007, -0.48658889,\n",
       "        -0.47999943, -0.4804352 , -0.48017599, -0.4809014 , -0.48086932,\n",
       "        -0.48095259, -0.48095123, -0.48095744, -0.48095956]),\n",
       " 'mean_train_score': array([-0.69314718, -0.6412946 , -0.67079105, -0.52380684, -0.47502503,\n",
       "        -0.46674403, -0.46228789, -0.46214818, -0.46206768, -0.46206619,\n",
       "        -0.46206531, -0.4620653 , -0.46206529, -0.46206529]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.01 0.01 0.1 0.1 1 1 10 10 100 100 1000 1000],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'param_penalty': masked_array(data = ['l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2'],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'params': ({'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}),\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  1,  3,  2,  5,  4,  7,  6,  8,  9], dtype=int32),\n",
       " 'split0_test_score': array([-0.69314718, -0.64371675, -0.66946739, -0.52816851, -0.48618151,\n",
       "        -0.4678555 , -0.46345792, -0.46129644, -0.46108763, -0.46088502,\n",
       "        -0.46086567, -0.4608487 , -0.46084658, -0.46084513]),\n",
       " 'split0_train_score': array([-0.69314718, -0.64085132, -0.66189542, -0.52529099, -0.47823631,\n",
       "        -0.47085398, -0.46684257, -0.46669378, -0.46662378, -0.46662226,\n",
       "        -0.46662151, -0.46662149, -0.46662149, -0.46662148]),\n",
       " 'split1_test_score': array([-0.69314718, -0.64113725, -0.67517494, -0.52518603, -0.47166717,\n",
       "        -0.47077815, -0.46426212, -0.46510588, -0.46463561, -0.46473286,\n",
       "        -0.46468564, -0.46469927, -0.46469725, -0.46469595]),\n",
       " 'split1_train_score': array([-0.69314718, -0.64188719, -0.67797602, -0.52532854, -0.47970631,\n",
       "        -0.47076994, -0.466927  , -0.46678157, -0.46671605, -0.4667146 ,\n",
       "        -0.4667139 , -0.46671388, -0.46671388, -0.46671388]),\n",
       " 'split2_test_score': array([-0.69314718, -0.64575466, -0.6680453 , -0.5512625 , -0.54752933,\n",
       "        -0.54301469, -0.56469894, -0.56426612, -0.56847816, -0.56834734,\n",
       "        -0.56880348, -0.56879096, -0.56882316, -0.5688357 ]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63908575, -0.66162164, -0.5135644 , -0.45564376,\n",
       "        -0.44783203, -0.44194586, -0.44184498, -0.44173181, -0.44173047,\n",
       "        -0.44172921, -0.4417292 , -0.44172919, -0.44172919]),\n",
       " 'split3_test_score': array([-0.69314718, -0.63856226, -0.67581323, -0.50975335, -0.45503212,\n",
       "        -0.44706595, -0.44365722, -0.44377717, -0.44397615, -0.44400066,\n",
       "        -0.44402727, -0.4440332 , -0.44403641, -0.44403656]),\n",
       " 'split3_train_score': array([-0.69314718, -0.64333601, -0.67874616, -0.5309552 , -0.4838255 ,\n",
       "        -0.47509784, -0.47066692, -0.47052445, -0.47044461, -0.47044314,\n",
       "        -0.47044227, -0.47044226, -0.47044225, -0.47044225]),\n",
       " 'split4_test_score': array([-0.69314718, -0.64152376, -0.67221592, -0.52767402, -0.47216377,\n",
       "        -0.47104103, -0.46581999, -0.46617809, -0.46606976, -0.46612355,\n",
       "        -0.46612344, -0.4661268 , -0.46612657, -0.46612722]),\n",
       " 'split4_train_score': array([-0.69314718, -0.64131272, -0.67371601, -0.52389506, -0.47771327,\n",
       "        -0.46916638, -0.46505711, -0.46489609, -0.46482214, -0.46482048,\n",
       "        -0.46481966, -0.46481964, -0.46481963, -0.46481963]),\n",
       " 'std_fit_time': array([  6.90797992e-05,   3.39473230e-05,   7.32740008e-05,\n",
       "          4.29026267e-05,   2.32594890e-05,   5.47904362e-05,\n",
       "          1.11469536e-04,   1.17447228e-05,   1.39179937e-04,\n",
       "          1.48214305e-05,   5.51774153e-05,   2.85931377e-05,\n",
       "          9.11694690e-06,   1.17398819e-05]),\n",
       " 'std_score_time': array([  5.14062746e-05,   2.73971892e-05,   1.33655797e-04,\n",
       "          6.08808459e-05,   2.37594133e-05,   7.01614636e-05,\n",
       "          4.57893790e-05,   1.18155591e-06,   1.36044909e-06,\n",
       "          1.41369150e-05,   2.56197820e-05,   1.72720398e-05,\n",
       "          1.82401277e-05,   1.37871097e-06]),\n",
       " 'std_test_score': array([ 0.        ,  0.00243715,  0.00305426,  0.0132657 ,  0.03205946,\n",
       "         0.03276814,  0.04294415,  0.04285084,  0.0445348 ,  0.04448689,\n",
       "         0.04466741,  0.04466183,  0.04467435,  0.04467945]),\n",
       " 'std_train_score': array([ 0.        ,  0.00138524,  0.00757197,  0.00566626,  0.00992507,\n",
       "         0.00965834,  0.01033384,  0.01031565,  0.01033125,  0.01033118,\n",
       "         0.01033136,  0.01033136,  0.01033136,  0.01033136])}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the complete results (list of named tuples)\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.479999434937\n",
      "{'penalty': 'l2', 'C': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与缺省模型得到的结果差异不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D8FWnNby+JGAYBqZh+BKDYWCaHecwj12XTLD4S39bAOztWEwTGyYW08TiSy9Y/cvWXivH\nho/OjtO/PRfKMIZEZ1teeCSUYQzaJMDAQmt9E0kZqX3u3x9SdbYHw9ENNVzCpeqsYRisea+EXVsr\nSEyOZcVlcxmT3XsCa16/lqPPP4PV5aYyI4oEu8Gih58e+rhMk1a7m+ZWF01tLpranL6frS6a2/0/\n23zbWu0nTi7VlcUCKQkxpCbGkJLk+5maGOv7nOj7fOSBe4gxPcz91d2Y/jsO0+zIT/5l03dHYnq9\nvrsTw4vpNTFN32fT60tOptfANA1Mrxe6JK2O7ZgmpuHF8GUx3zqvP5n5Mp5v2LFhgOHb1/Tvi2l0\nLpv+BOjPkr7vNQ0at2wFIG3+3BPnvOjtutJl/XGl1Lvtbumy4riv6vV61SWpd/+y3ib26LLYsncv\nAMnTp/Xy/SNDy94SAM7//a8G/B1SdXaARmqSCCcup4f3/r6Tw/sbyMxJ4oJL55KYHNvjvobTSfWf\nX6B57acY0TbeOy2FuXvt9Ny30TPTNHG4vP6Lvv/i3+a/6Ld2WW5z0tzmxujjF6bEuChSEmMYn5VI\nalKsPwkcSwApiTGkJsWSHB+N1XryONs8rQCk99L+kWTzxo8BKLw2/J+R9WXzTTcDUHjzd0McyeB0\ntCMYJFmIoGptdrDqlWLqatqYVJDBeV+bRXRMz3/tnIcPcfTxR3BXVhI9YQLPL7CTkjeJ5K2+32Dd\nHu+xC3/nnYCrMyk0d0kKLs/Jn9XERFlJTYohf2xKtzsB391A10QQHSXDc4WQZCGCpraqhVWvFNPW\n6mL2grEsO28qVuuJF17TNGn6aDU1L/0F0+Mh7byvULJkEg2lf+esnHkciG5idUYhjvs/Pun5bFYL\nKYkx5GUmHrvwJ53YFZSSGENcjE2KAwrRD5IsRFAcLK3j/X/swu3ysvTsAuZ/YXyPF2dvWxtVzz5N\n65bNWJOSyPvudSTNP4WXtjwJwIKsefw2uRKHLZaZk9JJS+rym3+Xu4GUxBgS46OxSgIQIigkWfTh\nxUfWAfDNm5aGOJKRY+eWcta8V4LVZuXLF8+mYEZWj/vZ95VQ8cRjeOrriJ+uyP3+DUSnp9PiakU3\n7GNyykRwxXM0Louxjhp+ctU5w9wSIUQHSRZiyJimyfqPyti64TBxCdEs/8YccsedOHzPNAzq336L\nun/8DUyTMRddTMaFF/nG8wNba4oxMSnMnseG3VUATG87PKxtEUIcT5LFMBquqrMvvPAshYWLmDVr\nTnAa0gOP28vqt/ZQuqeGtIx4Vlw+j5S0+BP3a2qk8k9P0L57F1Hp6eRe9wMS1Izj9imq8pUkX5A9\nj9++r7GaBgX28mFphxCiZ5IsQiDYVWe/9a3vBCHq3tnbXbz92g6qypvJm5DK+ZfMOaEYIEDbjmIq\nn3oSb0szifPmk/vd67AlJx+3T5OzhZLGMvJTJ9HWHMWRmjam2CuJM07+joMQIrhGbbJYu7qUsj3V\nPW6z2qwY/qqzrS2+mkQdzy5OJn9GNqedU9DnfoOpOnv33XfR1NREc3MT99zzII8++jDV1VXU1dVy\n+ulncP31N3H33Xdx7rlfpr6+jqKiDTQ3t1JefoRrrlnJBRd8NaDzBKqhrp1Vr2ynudHBtNnZnL18\nBrZuQ01Nj4fav79OwzurwGYj68qrSTv3vJ5rQfm7oBZmz2fdrkpAuqCECAejNlmE0mCqzgIUFi7i\niiuuoaLiKLNnz+VnP/s5TqeTSy65gOuvv+m4fVtbW7n33t9x+PAhbr/9liFNFkcPN/LOaztwOjwU\nnjaJxV+cfEICcNfUUPHkozjKyojOziHv+huJmzy51+/cXL0NCxbmZ83hV//cSVyMjcmOiiGLWQgx\nMKM2WZx2TkGvdwFdS2QEazTUQKvOAp2JJSUlhd27d1JUtInExERcrhO7ambM8D0PyM7OweVyDT5w\nv707q/hw1R4w4ewLFDPm5Z2wT8umz6l67hkMu53kU5eS861vY4078TlGh0ZnE6WNB8hPnUxNtUl9\ns5PT5+QStS/8iiEKMdoMa7JQSsUDLwLZQAuwUmtd022fh4Bl/u0AXwNcfR03kgym6iyAxT/hz6pV\nb5KUlMxPf/r/OHLkMG+88Te61/oa6hfPTNOkaN0hPv9kPzGxNr7y9TmMn5x+3D6G00nNS3+h6ZOP\nsMTEkPPd60g57fQ+Y9lavcPXBZUzj/U7fKOglszOxbFqSJsghBiA4b6zuBEo1lrfpZS6ErgT+GG3\nfQqBr2itaztWKKVuDeC4EWMwVWe7KixczC9/eSc7dxYTHR3N+PETqK0NXg71eg0+eXcve7ZXkpQS\ny4rL5pGRlXjcPs7ycioefwTX0XJiJ0wg7/obicnrvQR5V0X+Lqi5GbN5bc92UhNjmDkpnS3BaIwQ\nol+GO1ksA+71L78N/LzrRqWUFZgGPKGUygGe0lo/3ddxPUlPTzhh1rX+yMryjdKx2qzHfR6MlSuv\n7ly+9NKLAjrmpZf+ctzn3/3uWGHDrKxTWLXqzROO6brPMcl8/PFHAZ2zJw67m1ee28T+klrGTkjl\nyu99gaSUY3N7m6ZJ1fv/4vCTT2G4XORecD5TvrsSa4AP7uvbGyltOsCsrGk0tUTR5vBw0Rn55OSk\ndO4zFH8G4ULaEp4ipS3BaEfQkoVS6lrglm6rq4Am/3IL0P2NrUTgYeBBwAZ8qJTaBKT0cdwJGhra\nBxY4xz+zuPoHpwKERZnvgRiKEuUtTQ5WvVpMfU0bk6eO4UsXzcLudGOv8T0j8ba3U/3Cs7Rs/Bxr\nQgJ51/2A5IWF1DU5gcBmuFt92HeHNTd9Nu+uOwDA/CkZx8U+Uv8MeiJtCU+R0pbBtKO3RBO0ZKG1\nfgp4qus6pdTrQEckyUBjt8PagYe01u3+/VcD84HmPo4TQVJT6SsG2N7mYm7hOE47d+pxZbjtZWVU\nPvEo7toa4gqmknf9DUSPyez3eYqqtmPBwozUmfx53zZyMhKYnBsZv+UJEQmGuxvqM+AC4HNgObCm\n2/bpwEtKqQWAFV/303P4Hmyf7DgRBAdKann/jV143AannzuVeYvHd24zDYOG99+l9vVXwTDIWPFV\nxlx0MRZb/7v+6h0N7G8+yPT0qZQccOD2GCydlSNVYYUII8OdLB4FnlNKfYpvhNPV0PkAe5/W+g2l\n1AvAesANPK+13qmU2t/TcSJ4dmwu59MPSrDZrJx/yRymTD92t+Bpbqby6Sdp31GMLTWVvOt+QMLM\nWQM+15bqYgAWZs9j/Rrfi3inzs4ZXAOEEENqWJOFv3vpsh7WP9hl+T7gvkCOE0PPMEzWfVjK9o1H\niE+M5oJL55Kdd+whc/vuXVT86Qm8TY0kzJ5D7rXXE5WScpJv7FtRta8LKj9hOs8e3Er+2BRy0hMG\n2xQhxBAatS/liRO53V7+9c/d7N9bS/qYBC64bG5nMUDT66Xujb9Tv+pNsFrJvPRy0r98fmel2IGq\ns9dzoPkQM9KnsXNfG6YJS2bJXYUQ4UaShQCgvc3F268WU13RwrhJaXzl67OJjfMVA3TX1VHx5GM4\n9pUQlZlJ3vU3Ep/fdw2sQBRV+yrMLsyZx7/eq8RqsfCFmZIshAg3kiwEDbVtvPVKMS1NDtScHM5c\nrrD53y9p3bKZymeexmhvI2nRYnK+/V1sCUPXRVRUvR2rxUpeVAEHKrczJz+DlMTA3s0QQgwfSRaj\nXPnBBt55fScup4fFX5xM4WmTsFgsGG4Xta+8ROPqf2GJjib7298h9YtnDukIpVp7HYdajjAzYzrb\ndTMAS2flDtn3CyGGjiSLUUzvqOSjVRqAcy6cgZrju1C7KiuoePxRnIcPETN2HHk/uJHYceNP9lUD\n0tkFlT2PN9ZXERNtZcH0E9/R+Oucq7HZLBQOeQRCiEBJshiFTNNk02cH2fTpAWJiozj/ktmMm+Qr\nBti89jOq/vw8ptNJ6hlnkXXFVVhjY4MSR1HVNqwWKyneiVQ37uLUWTnExchfSSHCkfzLjGBlt/+Y\ngzYrk351bCSy12vw0duavTuqSE6NY8Vlc0nPTMRw2Kn68wu0rFuLNT6e3B/cRPLiLwQttur2Gg63\nHmX2mBls2+PrgpJRUEKEL0kWo4jT4ead13dy9FAj2XnJLL90LgmJMTgOHaTi8UdwV1URNyWfvOtv\nJDorK6ixFPlfxDslay4vf1xFUnw0s6dkBPWcQoiBk2QRwT5OOReLBb4DNDfaWfVKMQ117UyZnsm5\nX51JVJSVhg/ep/bVlzA9HtK/spzMr38DS1Tw/1oUVW/DZrER0zaW5nbN2QvHEWUb3DsbQojgkWQx\nClQdbebtV4uxt7uZv3g8S84uwGxv4+hjf6Jt21ZsycnkXvt9Evs5n8aA42mrpry1gjljZrJlj68m\n5MlGQd1302lDUj1XCDFwkiwinBcbb/zfVrxegy+eN405heNo36upfPJxPA31xM+YSd51PyAqLW3Y\nYuoYBTVvzBxe/KCWzNQ4CsYNrmSIECK4JFlEMI8lCo8lmigLnP+NOUzKz6Dun/+g7o2/g8XCmK9/\ng4zlKwZdsqO/iqq3E2WNwtuYjdNdynmzx0uFWSHCnCSLCHWgpBaPNQZMg4uvWUB6nIcjD9yLXe8h\nKiODvO/fSPy0acMeV0VbFUfbKpmXOZui3b4uqFNH0Yt4hY/8QbrUxIgkySICeb0Gaz8sBdMk1nQS\nX13KwaefwtvaQuKCheSu/B62pKSQxNbRBTUrbRbP769nYnYS4zIT+zhKCBFqkiwi0K4tR2mqt2Mz\n3eTXbeHo71/CEhVF9tXfJPXsc0Pa5dPRBWWvGYPXaGHJ7NFzVxFpIukuKVLaEsx2SLKIME6Hm42f\nHiA6xsacstVkOCqJzs0l7/obiZs4KaSxHW2tpLKtivlZc9i0rQELcKq8iCfEiCDJIsJsXnsQp8PD\nnPQmMhyV1CROZOmd/4k1Li7UoVFUvQ2A6YkzWX+kiRkT00hPDk4pESHE0JK3oCJIU4Od4k3lJCVF\nkVn0T7BaGZfQHhaJwjRNiqq3E22NprHCN0xXuqCEGDkkWUSQ9R+VYhgmyr4Hm9eDNTExbIakHm2r\npKq9hjljZrBpdz1RNguLVHBLigghhk5A3VBKqQJgCfB/wOPAAuAWrfWnQYxN9EPF4UbKdC1ZaTZS\nN31Kwuw5OCuOhjqsTkVVvi6oibHTWVvbRuH0LBL8M/EJIcJfoHcWzwAu4GvAdOBW4P5gBSX6xzRN\n1q4uBaDgyBosVitZV1wVNncVpmmyuXobMdZo6o743tReMlsebAsxkgSaLOK01q8AFwJ/1lqvAeTX\nwjBRsqua6ooWJqZ5SKzcS9rZ5xI7dlyow+p0pPUoNfY65oyZyabd9cTHRjGvYEyowxJC9EOgycKr\nlPoGvmTxplLqYsAbvLBEoDxuLxs+LsNmszBh19tYk5IYc9HFoQ7rOB0v4uVYp9LQ4mSRyiI6yhbi\nqIQQ/RFosrgeWAH8m9a6ArgSuC5oUYmAbdt4hNZmJ/mx9cS115F58SXYEsPnjWjTNCmq2kaMLYaq\ng764ZBSUECNPQMlCa10M/D+t9WtKqS8Ca4DSoEYm+tTe5mLL+kPExVoZu2MVMeMnkHrGWaEO6ziH\nW8qpddQzJ2MmRbqB9ORY1MThq3ArhBgaASULpdSjwJ1KqVn4RkQtBJ4PZmCibxvX7Mft8jLVroky\n3GRfdc2wV5Dty2b/i3jp3snYnR5OnZmDNUwevAshAhfoleULwM3A5cBTWutrgdDWjhjl6mpa2b2t\ngtQEyCpbS9KixSSoGcftk3/PAyx68rEQRXjsRbw4WyzlZR1dUDIKSoiRKNBkYfPv+zXgbaVUApAQ\ntKhEn9atLsU0Ib9iLbboKLIuvTzUIZ3gYMth6h0NzMqYSXFpA2MzE5mQHZpqt0KIwQk0WTwPVAAH\ntNYbgM34Xs4TIXCorI7D+xvISXCRXrOX9K8sJzoz/N6GLqryjYJKcEzE4zVZMisnbN79EEL0T6AP\nuB8E8oBvK6XSgC9qrR8KamSiR4ZhsHZ1KRYLTCl5j+j0DDKWrwh1WCc41gUVx6ESX7HAJVJhVogR\nK9ByH/nAX4ECfAnmgFLqcq11SX9OppSKB14EsoEWYKXWuqbbPg8By/zbwdf11QwcATrOt05rfUd/\nzh0pdm8lJDYFAAAeAklEQVSrpKG2nUnR9STaa8n85g1YY8Ovcuv+5kM0OBs5ZcwprF/XwtTxqWSm\nxYc6LCHEAAVaovxx4F6t9asASqnLgSeBs/p5vhuBYq31XUqpK4E7gR9226cQ+IrWurZjhVJqKlCk\ntf5qP88XUVxOD5+v2U+UzcKEve8TN3UayV84NdRh9aijHHlM63hMPCyVuwohRrRAn1lkdiQKAK31\ny0DGAM63DHjHv/w28KWuG5VSVmAa8IRS6jOl1Pf8mwqBcUqpD5VSq5RSagDnHvGK1h/C0e5mir2E\nWMNB9pXXhOUzAMM02FJdTHxUPGV7Y7BZLSyakR3qsIQQgxDonYVTKbVQa10EoJQqBNpPdoBS6lrg\nlm6rq4Am/3ILkNpteyLwMPAgvhFYHyqlNuF7uP5rrfUrSqll+LqyFp/s/OnpCUQNoqREVlbygI8N\nhsb6drZvPEJiLIwrXUf2uecwYfHcgI4d7rbsqSml0dnE4txFfLK2nUUzc8ifNPhaUOH2ZzIY0pbw\nFCltCUY7Ak0WPwJeU0rVAxZ8dxVXnuwArfVTwFNd1ymlXgc6WpEMNHY7rB14SGvd7t9/NTAfeA3w\n+L/3U6XUWKWURWtt9nb+hoaT5rKTCse5eN9/Yxdej8GUmg1Ex8WQtPyigGIMRVtW710HgLs6CzBZ\nOG3MoGMIxz+TgZK2hKdIactg29Fbogl0NNR6fKXJvw2sBKb71/XXZ8AF/uXl+MqGdDUd+EwpZVNK\nRePrtioC/gtfwkIpNR84fLJEEWmqjjazb1c1adEusut2k3HhRUSldr8pCw++LqjtJETFs3d3NLHR\nNhZMDb9hvUKI/jnpnYVS6hmgx4uyUgqt9fd62nYSjwLPKaU+xTc/xtX+77oV2Ke1fkMp9QKwHnAD\nz2utdyqlfgO8qJRage8O4zv9PO+IZZoma/+1D4D8A/8iJieX9HPPC3FUvSttPECTq4U5KfPZ2ORi\n6ewcYmOkwqwQI11f3VAfDeXJ/N1Ll/Ww/sEuy/cB93Xb3oCv6u2oU6ZrqCxvJtdST7q9iqzv34Il\nKtDew+HXUY7c05AHSIVZISLFSa86WuvnAJRSE7ttMgF7sIISPl6PwboPy7BaYMqBD0mYM4+kefND\nHVavDNNgS812EqMS2FtsIyXByqzJ6aEOSwgxBAIdOvt3fCXJ/+Zf3gdsVkqVKqXODVZwo13x5iO0\nNDmYYC8jwWgn+8qrQh3SSe1r3E+Lq5UJcdNos3tZPDMHW5hVwRVCDEyg/5KPAEu01oVa64XAImAT\nvpfyfh2k2EY1e7uLzWsPEmMzmXR0PennfImY3LxQh3VSHeXInTW+F/CkwqwQkSPQZDFFa72544N/\nMqQCrfVhAh9+K/ph06cHcTm9TK4tIi4xloyvfi3UIZ2U1/CytbqYpOhE9u2xkp0WT35eSqjDEkIM\nkUAv9KX+EUkv4EswVwP7lFJLkbm4h1xDXRs7t5STaHUxrm4nmd9eiS0hvCvClzSW0epuY3r8fGrc\nvruKcHy7XAgxMIHeWXwbX2L5P+BZfC/mfRfIB24ISmSj2LoPyzBNKChfQ/zECaQs+2KoQ+pTxyio\n1spMQEZBCRFpArqz0Fo3K6XuAT7GV4Zjnda6BfhzMIMbjY4caODgvjoyjEYy2w6TffN/ht1Uqd15\nDS9ba4pJik5i/95oJucmk5sR3ndCQoj+CXQO7q8AW/G9DLcS2K6UujCIcY1KhmGydrXvBbyC8k9I\n+cKpxE+bHuKo+ra3oZQ2dzvZTMEw5a5CiEgU6DOLu4FlWuv90Dm/xevAm8EKbDTau6OSuuo2xtoP\nkmq2khmGU6X2pKMceWN5JhYLnDpTKswKEWkC7d+I7kgUAFrrsn4cKwLgdnnY8Ml+bBaTKZUbyFi+\nguiMwVdqDTZfF9QOkqKSKD8Qw6xJ6aQmhd9kTEKIwQn0zuKQUupHHKsiex1wMDghjU5bNxymvdXF\nlIbtJKXGk/7l80MdUkD2NOyj3WNnonUuNVikC0qICBXo3cG1wFKgDDjgX74+SDGNOq0tTrZuOEys\nxc3E+mKyLr8iLKdK7UlHF1TtoQyio6wsnC4VZoWIRIGOhqoGrghyLKPW5x+X4fEYTKvaQPK0ApIK\nTzqvU9jwGB621ewkKSqZmqNxLJ6RSXysvKMpRCTqq0T5fnopUQ6gtc4f8ohGmZrKFvSOKpK9zeS1\nlpF15X+NmJfZ9tSXYPfYGWvO8XdBSXkPISJVX78GnjUcQYxWpmmydnUpAFMr15F2xhnETZwU4qgC\n1/EiXvWBNBLjopibH/4P5IUQA9NXifITHmIrpZ7QWsvziiFwoKSOo4caybSXk2lpYszFl4Q6pIC5\nO7ugUqipSeTMU7KJsskAOSEi1UD+dS8a8ihGIa/XYN2HpVgwmVr9OWMuupio5JFTeG9P/V4cXgcJ\n9gmAhSWzpAtKiEg2kGQxMjrUw9zOLUdparAzrmkP6ZmJpJ11TqhD6pfNVb5RUFX708hIiWXahLQQ\nRySECKaBJIvrhjyKUcbpcLPp0wNE4WFK3VayrrgqrKdK7c7ldbO9didJthQcjUmcOisH6wh5KC+E\nGJiArlBKqWfoMipKKdUxrepu4EmttSs44UWmzZ8dxOnwMLV2C+lzZ5I4Z26oQ+qX3fUap9dFomMq\nYGHpLHkRT4hIF+idhQdIxTel6t+BeCAbmA48FpzQIlNTQzvFm8uJ97YxoWUvWZeH91SpPekYBVVV\nlsb4rETGZyeFOCIhRLAF2vexQGvd+WBbKfVPYIPW+nKl1LbghBaZ1n9UhmGYFFR/zpjzziMmZ2Q9\nGPZ1Qe0i0ZpKbWsySxbJXYUQo0GgdxaJSqmuV4VsfHcXINOqBuzo4UbKdC2pzhrybA1krPhqqEPq\nt511e3B5XVibxwIWTp05spKdEGJgAr3Q/xewWSm1Ft/kR4uAHyql7gLeD1JsEcU0Tdb+y/cC3rTq\nDWRdfRm2+Pg+jgo/HbWgag6kM31CGmNS40IckRBiOAR0Z6G1fhmYz7FpVRdqrf8G/F5r/aPghRc5\nSnZVU1PZQk5LGdl5yaQsPT3UIfWb0+tiR+1uEi2pmO3JUt5DiFEk0JnyYvBVmf06vqlVb1JKxWit\n64MZXKTwuL1s+KgMq+mloG4z2VddE/ZTpfZkZ90eXIYboyEPm9XKIiWTHAkxWgR6xfojkAQsBNzA\nVI7NbSH6sG3jEVpbnExo3En2onnEF0wNdUgDUuR/Ea/h8BjmFYwhKT46xBEJIYZLoMmiUGv9n4Bb\na92Obx7uBcELK3K0tzrZsu4g0YaDKW2azG+MjKlSu3N4nOyo20M8qZj2JJnkSIhRJtBkYfq7ojpe\nzMvkJKXLxTGfrzmA222QX7uF7OXnE52eHuqQBmRH3W7chht3bQ5xMVHML5AKs0KMJoEmi4eAD4Bc\npdTvgE3Ab4MWVYSoq25lz/YKEl2NTIppIP3LXwl1SAPW8SJea0UWhSqLmGhbiCMSQgynQIfO/gVI\n8//XADyA761u0YuOuSpME6bVbiT7O1dgjY4JdVgD4vA42Fm3h3gjDbs9WbqghBiFAk0WfwYm4asF\n1dH9ZALP9+dkSql44EV8L/W1ACu11jXd9lmO770OC7AZ+Dcgrq/jws2hsnqOHGggo72c8RNTSFqw\nMNQhDVhx7W48hgdvdTapSTHMnDgyu9KEEAMXaLKYp7WeMQTnuxEo1lrfpZS6ErgT+GHHRqVUMnAf\ncJbWulYp9VN8z0e+dbLjwo1hGKxbXQqmybS6TWRff+uImSq1J5v9L+LZq7NZNjcHq3XktkUIMTCB\nJovdSqk8rXXFIM+3DLjXv/w28PNu208DioEHlFL5wJ+01jVKqb6OO0F6egJRUQPvV8/KSh7wsZvW\nHqChrp2xzXuZeu6pjF8wa8DfNRQG05Z2l53d9XuJM9KxO5JYfnr+oL5vMEJ13mCQtoSnSGlLMNoR\naLJIALRSagfg6Fipte51xh6l1LXALd1WVwFN/uUWfJVsu8oEzgZOAVqBNUqpdUBKH8edoKGhva9d\nepWVlUxNTcuAjnU5Pax+azc2w81Uuybxy/894O8aCoNpC8CGis14DA/OyixyMxJIibWGpD2DbUc4\nkbaEp0hpy2Db0VuiCTRZ/Kq/J9RaP0W3F/eUUq8DHZEkA43dDqsDNmqtK/37f4IvcTT3cVzYKFp3\nCIfDQ0HDdsZddAG2pJFdvrtjFJS7Npsli3NGdHeaEGLgAkoWWuuPh+h8nwEXAJ8Dy4E13bYXAXOU\nUpn4EsIS4MkAjgsLzY12tn9+iFh3KwXxjaSeeXaoQxqUdrevCyrGnYbd4ZsRTwgxOg13efFHgeeU\nUp8CLuBqAKXUrcA+rfUbSqk7gHf9+7+std6hlCrr6bhws+Hj/XgNmFFXRN4PrsJiG9nvImyr3YnX\n9OKoyiJ/bAo56QmhDkkIESLDmiz8pUIu62H9g12W/wr8NZDjwklleRP7dleT4qihYGo6CTND+1B7\nKHSUI/fW5bLki3JXIcRoNvJKn4Yh0zRZ+0EJANMbisi64ooQRzR4be529tSXEOVKw+JK4gsyyZEQ\no5rMcjcEynQNVRWtZLUeYMqZhcRkjfzS3dtqdmKYBs6qbGZNSSclcWS+fS6EGBpyZzFIHo+Xdf8q\nwWJ6Ua69ZFxwYahDGhKdXVD1uSydJeU9hBjtJFkMUvHmclpa3Exo3M2kr1+INW7kTzPa6mpDN+zD\n6kgj2khiwfTMUIckhAgxSRaDYG93sXnNfqK8DmaktpB86pJQhzQkttXs8HVBVeewYFoWcTHSWynE\naCfJYhA2rjmA22OSX7+VsVdeMSKnSu1Jx4t43vpclsi7FUII5AH3gDXUtbFrSzkJrmZmzsokPj8/\n1CENiRZXK7phH5b2NBJtKcyekhHqkIQQYSAyfhUOgbXv78XEwrSmbWR/49JQhzNkttbswMTEVZvD\n4pnZRNnkr4gQQpLFgBw50MChA02ktVegzl1EVFpaqEMaMkVVMgpKCHEiSRb9ZBgmn727G0yTmd5S\n0s87L9QhDZkmZwsljWWYrWmMiU+nYFxKqEMSQoQJSRb9pIsrqG9wkdeyj6mXrcAaHR3qkIbMtppi\nTEzcdbksmS0VZoUQx0iy6Ae3y8OG1SVYDTezM9tJnH9KqEMaUsePgpIuKCHEMZIs+qFo7UHsTpNJ\nTbuYeOWlEfWbd5OzmX2N+zFa0pmYkcnYzMRQhySECCMydDZArc0Otm04RIzHzrz5WcSOHRfqkIbU\nlmpfF5RH7iqEED2QO4sArf9A4zUtTG3bSc7XvhbqcIZcUfU2MMGoz5FJjoQQJ5BkEYCayhZK9jaQ\n5Kxj7pcLsSVGVhdNo7OJ0qYDeFvSmTE2j/Tk2FCHJIQIM5Is+mCaJmve2gHALMtB0s88K7QBBcGW\n6mIAvPV5Ut5DCNEjSRZ92L+3hqoaJ5lth5h5xQURU/+pq6IqXxeUpSmPQjXy5+IQQgy9yLvyDSGv\n12DtO7uwmAbzcp0kqBmhDmnINTgaKWs+iLclg/mTxpIQJ2MehBAnkmRxEjs+P0iLHca1lJB/xcWh\nDicoOt+t8L+IJ4QQPZFfI3vhsLvZuGY/UV4PCxdmE52ZFeqQgqKjCyqmfRzzCsaEOhwhRJiSO4te\nfP7BbtyGlQJHCXlfvSDU4QRFnb2eAy2H8TaPYVHBeKKjbKEOSQgRpiRZ9KC+to1dO2uJdzez4IKF\nWGMjcyjplpqOUVC5LJktL+IJIXonyaIHb/95PSZWZkYfJW1JZEyV2pNNlVvBtJDsnoCaGDll1oUQ\nQ0+SRTflB+opPdROqr2KeVd8JaLqP3VVa6/jcGs53uYMlkyfiDVC2ymEGBqSLLp594W1AJwyzkP8\nlCkhjiZ4jo2CypNRUEKIPsloqG5SnLXEtR5k+o3fDHUoQbWpchumaSHLOoUJ2UmhDkcIEeYkWXRz\nSttmbDYrUampoQ4laKrbaylvO4rRlMlpMyZEbFebEGLoSDdUN9aYGKwxMaEOI6iOn+RIuqCEEH2T\nO4tu8u95gKysZGpqWkIdStBsrNiKaViYFD+NzLT4UIcjhBgBhjVZKKXigReBbKAFWKm1rum2z3Lg\nvwALsBn4N/+mI0CJf3md1vqOYQk6wlS1VVNpr8RozuL0mRNCHY4QYoQY7m6oG4FirfUXgeeBO7tu\nVEolA/cBF2qtTwUOAJlAAVCktT7L/58kigEq8pcjN+vzWDRDKswKIQIz3MliGfCOf/lt4Evdtp8G\nFAMPKKXWAFX+O49CYJxS6kOl1CqllBq2iCPM5xVbMA0LKk2RnBDZz2aEEEMnaN1QSqlrgVu6ra4C\nmvzLLUD3IUeZwNnAKUArsEYptQ6oAH6ttX5FKbUMX1fW4pOdPz09gahB1DrKykoe8LHhpqMtR5or\nqHZUYzRls3zJ9BHXxpEW78lIW8JTpLQlGO0IWrLQWj8FPNV1nVLqdaCjFclAY7fD6oCNWutK//6f\n4EscbwIe//d+qpQaq5SyaK3N3s7f0NA+4Ngj6QF317Z8UOZ74dDSPJb8nKQR1cZI/TMZ6aQt4Wew\n7egt0Qx3N9RnQEcJ1+XAmm7bi4A5SqlMpVQUsATYhe+B948AlFLzgcMnSxSiZ+uPbsU0rMzPnEVs\ntFSYFUIEbriHzj4KPKeU+hRwAVcDKKVuBfZprd9QSt0BvOvf/2Wt9Q6l1G+AF5VSK/DdYXxnmOMe\n8Y62VlLvqsVozGHZQhkFJYTon2FNFlrrduCyHtY/2GX5r8Bfu21vAFYEPcAItqlqGwAxbeOYOTk9\nxNEIIUYaeYN7FDBNk/XlWzANK4vHzsVmlT92IUT/yFVjFDjaVkmTpx6jMYvTZo8PdThCiBFIksUo\n8HnFFgASHRPJz0sJcTRCiJFIkkWEM02TDUe3YnptnDZxnlSYFUIMiCSLCHew8Qgt3ka8jVmcPkdG\nQQkhBkaqzkawn6/9Ne1OJwBjjCnkZiSEOCIhxEglySKCmaaJ0+PGxMayKfNDHY4QYgSTbqgI5jW9\nmDY3RmMWp88aF+pwhBAjmCSLCOZwGgCMtU0jNSk2xNEIIUYy6YaKUKZp4vS6wGLjrKmnhDocIcQI\nJ8kiwlS111BUtY3N1duwRLnx1OWy+Iy8UIclhBjhJFlEgFp7HZurtlFUvZ0jrUd9K00r3oZsjKZM\n4mPlj1kIMThyFRmh6h0NFFVvZ3PVNg61HPGtNC14G7Pw1udhac7GiG8iZvy+0AYqhIgIkixGkEZn\nky9BVG7jQMsh30rTgrcpE299LlGtY5k3OZfC07OYVzCGn3z0P4C8sS2EGDxJFmGu2dXClupiNlVu\npaz5gG+lCd7mMXjrc4ltH8fi/LEUnpnNrMnpxHSZ1Cg9OQ6bVZKFEGLwJFmEoVZXG1tqfAmitGk/\nJiamCUZLOt76PJJdE1iYP57CBVlMn5gmJceFEEEnySJMtLvb2Vqzk88rtrCvqRQT36yx3pY0vPW5\npHsns3jqRBZ+IYspeSlYpSCgEGIYSbIIIbvHzvaaXawrL2JfcykmvpfojNZUvPW55FgKWDx1EgtP\ny2JcZmK/K8b+z2l3RMwk9EKI0JJkMcwcHic7anfx2ZEiSppLjiWIthS89bmMj5nGqQVTWHhGFllp\n8SGOVgghfCRZDAOX10Vx7W7WHNpMaUsJBl4AjPYkjIY8JscrlhYUsODsTCnLIYQIS5IsgsTtdbOj\ndg+fHNzEvpYSDIsHAMOeCI15TEucydKp05j3pTEkxkWHOFohhDg5SRZDyGN42FGj+XD/RsraSjAs\nbgAMZwLWpsmo5FmcNnU6cwvGENtliKsQQoQ7SRaD5DW8bK/2JYj97SUYFhcAhiuOqJaJzEqdw7IZ\nipmTMoiyyRBXIcTIJMliALyGl+1Ve1ldtpED9hIMq282OtMdS3RrAXMy5nDWnNkUjE+VIa5CiIgg\nySJAhmmw5ajmw/2bOGjfi2HzJwhPLPF2X4I455S5TMxJ7vcQVyGECHeSLE7CMA02H97LR/s3cshV\ngmFzAGAa0STaC5g3Zi5fmj+PvDFJIY5UCCGCS5JFN4Zh8NHuYt4s/pQj7hKMKDsAphlNsj2fUzLn\ncd7MU8hMSQhxpEIIMXwkWXRz+3u/pz3GNyeEaYki1ZnPwqx5fHnmQlIT40IcnRBChIYki27GxGQR\nbyRwSuYcvjxjIUlxkiCEEEKSRTc/O+caqackhBDdyMB/IYQQfRrWOwulVDzwIpANtAArtdY1Xbaf\nAvyuyyFLgIuBj092nBBCiOAa7juLG4FirfUXgeeBO7tu1Fpv1VqfpbU+C/gj8JrW+p2+jhNCCBFc\nw/3MYhlwr3/5beDnPe2klEoEfgmc0Z/jukpPTyAqauD1l7Kykgd8bLiJlLZESjtA2hKuIqUtwWhH\n0JKFUupa4JZuq6uAJv9yC5Day+HXAq9orWv9n1MCPK5TQ0N7v+LtKpIecEdKWyKlHSBtCVeR0pbB\ntqO3RBO0ZKG1fgp4qus6pdTrQEckyUBjL4dfA1za5XNzgMcJIYQIguF+ZvEZcIF/eTmwpvsOSqlU\nIFZrfbg/xwkhhAie4X5m8SjwnFLqU8AFXA2glLoV2Ke1fgOYDhwI5DghhBDDw2KaZqhjEEIIEebk\npTwhhBB9kmQhhBCiT5IshBBC9EmShRBCiD5JshBCCNEnSRZCCCH6JMlCCCFEn2Tyox74Cxn+H5CO\n7yXAlVrr8tBG1X/+t+FfxFdbKwa4VWu9LrRRDY5S6uvAZVrrEfdiplLKCjwCzAecwHVa632hjWrg\nlFKnAvf4q0SPSEqpaOBpYDIQC/yv/+XgEUcpZQOeBBRgAjdorXcM1ffLnUXPvg9s1lqfge9i+9MQ\nxzNQtwL/0lqfCXwHX9n3EUsp9RDwa0bu39uLgTit9VLgZ8ADIY5nwJRSPwX+BIz0eYe/CdT5pz84\nH/hDiOMZjK8CaK1PxzeNw91D+eUj9R9dUGmtf8ex/6MnMnILF/4WeNy/HAU4QhjLUFiLb26TkWoZ\n8A6A1no9sCi04QxKKXBJqIMYAq9wbMoDC+AJYSyDorX+O3C9/+Mkhvi6Neq7oXoppf5drfVGpdRq\nYC5w3vBH1j99tCMX3x3Sj4Y/sv47SVteUkqdFYKQhkrXUvsAXqVUlNZ6xF2gtNavKaUmhzqOwdJa\ntwIopZKBVxnhE6tprT1KqeeAr3N85e5BG/XJoqdS6l22naOUmgG8BRQMa2D91Fs7lFJzgb8Ct2mt\nPx72wAbgZH8mI1zXUvsA1pGYKCKNUmoC8DfgEa31/4U6nsHSWq9USt0ObFBKzdJatw3F90o3VA+U\nUncopb7l/9gKeEMZz0AppWbhu82+Wmv9dqjjEcdK7SullgDFoQ1HKKVygPeA27XWT4c6nsFQSn1L\nKXWH/2M7YPj/GxKj/s6iF0/jK4l+LWADvhvieAbq1/geQD6klAJo0lp/LbQhjWp/A85TSq3F1z8+\nUv9eRZL/xDfq8edKqY5nF8u11vYQxjRQrwPPKKU+AaKBHw1lO6REuRBCiD5JN5QQQog+SbIQQgjR\nJ0kWQggh+iTJQgghRJ8kWQghhOiTJAshBkgpdZZS6qMBHjteKfVMl8/fVkptVEptVUptV0r9R5dt\nzyulxg1ByEIMmCQLIULjd8A9AEqp6/GVYrlIa30KcAbwTf97Pvj3+21IohTCT17KE2KQlFLTgSeA\nDKAN+A9/Ta7xwJ/xvfRVDJyptR6vlJoKjNVa7/F/xZ3At7XWFQBa60al1Ep8taTQWu9USk1WShVo\nrUuHt3VC+MidhRCD9yLwe631PHwFEF9VSsUCDwEv+de/CnR0JV0IfAqglMoEJgAbun6h1nq31rrr\nuk/9xwkREpIshBicJGCq1vp16Cw9Xo9vAprzgBf86//GsZLR04Aj/uWO2j2WPs5z0H+cECEhyUKI\nwbFy4oXegq+L10vP/8YM/PMmaK3rgTK6zW2hlDpTKfWbLqvcDGFROCH6S5KFEIPTDJQqpS6Bzmqy\nucAO4H3gav/65UCa/5hSfJPTdLgPeMA/70hH19QDQNcpV6d0+yzEsJJkIcTgfRP4D6VUMb5pOS/R\nWrvwjXD6hlJqC3AFx7qh3gTO6jhYa/0Yvu6q95VS24APgWe11n/qco4zgX8GuyFC9EaqzgoRJP53\nJT7QWu9SSi0EntRaF/q3vQ78Qmu9I4DvmQ/cqbW+LLgRC9E7GTorRPCUAH9RShn45j//fpdttwD/\nDawM4Ht+Cvx46MMTInByZyGEEKJP8sxCCCFEnyRZCCGE6JMkCyGEEH2SZCGEEKJPkiyEEEL06f8D\n9bCGidHgUbAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab75d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图给出了L1正则和L2正则下、不同正则参数C对应的模型在训练集上测试集上的正确率（score）。可以看出在训练集上C越大（正则越少）的模型性能越好；但在测试集上当C=0.1时性能最好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 线性svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn import metrics\n",
    "\n",
    "SVC1 = LinearSVC().fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        99\n",
      "          1       0.36      1.00      0.53        55\n",
      "\n",
      "avg / total       0.13      0.36      0.19       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[ 0 99]\n",
      " [ 0 55]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/py27/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "y_predict = SVC1.predict(x_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, metrics.classification_report(y_test, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  正则参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, x_train, y_train, x_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(x_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(x_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.402597402597\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa83590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, x_train, y_train, x_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('SVM_diabetes.png' )\n",
    "\n",
    "pyplot.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出最好的是在c=1000时表现最好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RBF核SVM正则参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_RBF(C, gamma, x_train, y_train, x_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(x_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(x_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.357142857143\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n",
      "accuracy: 0.642857142857\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-2, 2, 5)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, x_train, y_train, x_test, y_test)\n",
    "        accuracy_s.append(tmp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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49FCGRqWzVULyPCZNQZgqNjySZOcYnMEn+PcuCckLVE6nk5yidQRZgpibNtvscjxmsVjI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\nvtcFJidHU15e0+vtfUXq6hl/1GXDRpx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab108d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('RBF_SVM_diabetes.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相比线性模型，结果已经要优秀很多了。最优值已经达到0.6428"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
